Integrated Biosensor and Simulation System for Diagnosis and Therapy

ABSTRACT

BioMEMS/NEMS appliance biologically monitors an individual, using biosensors to detect cellular components. Data is simulated or analyzed using systems-biology software, which provides diagnostic or therapeutic guidance.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/646,682 filed on Aug. 22, 2003.

BACKGROUND

1. Field of Invention

Invention relates to sensors and related software for monitoring oranalyzing biological hosts or material.

2. Related Background Art

Various sensors are used to detect or measure macroscopic or molecularphysiology in humans or other biological host. Additionallysystems-biology software provides computational modeling of molecularstructures and interactions for genomics, proteomics, metabolomics,transcriptomics, computational chemistry, pharmacogenomics, or otherpurpose. Such tools, however, are not easily or automatically integratedor reconfigurable for interdisciplinary diagnosis or therapy.

SUMMARY

Integrated biosensor-simulation system combines one or more sensor todetect various conditions in biological target or host, and softwareprogram or simulator using system-biology model and sensor dataadaptively to provide therapy, diagnosis, or other automated feedback.Preferably one or more sensor is reconfigurable by the simulator.Optionally food material for consumption by the biological target issensed for application to the simulator, which may apply certainregulatory condition. Switch couples simulator programmably to sensors.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 a shows sensor device according to aspect of present invention.

FIG. 1 b shows sensor network according to aspect of present invention.

FIG. 2 shows sensor system according to aspect of present invention.

FIG. 3 a shows systems-biology software according to aspect of presentinvention.

FIG. 3 b shows systems-biology software and data according to aspect ofpresent invention.

FIG. 3 c shows system biology software and sensor according to aspect ofpresent invention.

FIG. 4 a shows system biology software according to aspect of presentinvention.

FIG. 4 b shows therapy according to aspect of present invention.

FIG. 4 c shows therapy reservoir according to aspect of presentinvention.

FIG. 4 d shows sensor reconfiguration according to aspect of presentinvention.

FIG. 5 shows DNA sensor according to aspect of present invention.

FIG. 6 shows diagnosis or therapy method according to aspect of presentinvention.

DETAILED DESCRIPTION

FIG. 1 a architectural diagram illustrates implantable network biosensor100. It is contemplated herein that sensor 100 may also operate withoutbeing implanted in biological host, but instead through external contactor attachment thereto. Optionally multiple coupled sensors 100 mayprovide fault-tolerant back-up or recovery facility, in case one or moresensors fails or malfunctions. Sensor 100 may be provided inside a host,e.g., mouth, larynx, blood vessel, vein, nose, ear, eye, heart, brain,lymph node, lung, breast, stomach, pancreas, kidney, colon, rectum,ovary, uterus, bladder, prostate, or other organ or using portablemobile application externally, e.g. skin, fingernail.

Sensor 100 includes sensor unit 111, controller 112, therapeutic unit113, and power module 114. Sensor 100 components may be interconnectedor communicate with other components using electrical, electronic, orelectromagnetic signals, e.g., optical, radio frequency, digital, analogor other signaling scheme. Power module 114 provides electrical energyfor sensor 100 to operate.

Generally biosensor 100 may sense individual genome, proteome,metabolism, transcription, translation, blood pressure, carbohydrate andoxygen concentrations, or other factors as described herein. Data isprovided by sensor 100 to integrated network 103 that appliessystems-biology software 104 to verify, model, or analyze, for example,relative sequences, 3-dimensional structure, molecular interactions, oroverall cellular and physiological environment.

Systems-biology software 104 processes information and determinestreatment dynamically for individual real-time physiological condition.Analysis report and other patient instructions are transmitted remotelyas telemedicine service to network 103, which provides tasks tocomponents, such as pharmaceutical or biopharmaceutical reservoirs 109,reconfigurable biosensors 102, wireless telemetry system 106,therapeutic manufacturers 108, or other applications.

Sensors 102 may be hardware-reconfigurable or software-programmableaccording to user or systems-biology programming or report instructions.Ongoing or intermittent scheduled or random sensing events occursbetween therapeutic components and pre-programmed and reconfiguredmicro/nano biosensors 102, along with proactive or reactive feedback topatient or user from systems-biology platform 104. Preferably sensingprocess employs micro or nanoscale sensor 102 structure for minimalintrusion to individual health or physiology.

Optionally sensor system 100 provide wireless (RF) signal coupling withother sensors 100, such that communication occurs between differentorganisms having sensor 100. For example, sensor 100 may be implanted inpregnant host and another sensor 100 implanted in such host baby.Communication between sensors 100 may provide effective biologicalsensor signal transmission between separate hosts or organisms. Sensor100 may be accessible according to IEEE 1451 network interface format.

Another example for multi-host communication implements sensors 100 forcommunication between separate related individuals, such as potentialsexual partners, where one partner sensor 100 may sense sexuallytransmitted disease (STD) in such host, then such information isprovided electronically to other host sensor 100 to produce properantigens and antibodies to combat the STD.

Sensor unit 111 uses positioning device or chip 101 to position, locateor immobilize effectively target sample for analysis or sensing. Themanipulated targeted sample comprises a biological molecule, organic orinorganic substance, such as cells, tissue, nutrients, chemicals,intracellular materials, extra-cellular materials, charged ions,pharmaceuticals, or molecular materials affecting host physiology.

Sensor unit 111 comprises multifunctional biosensor platform 102 forsensing and monitoring multiple biological materials, concentrations,inorganic or organic materials, cellular material, genetic material,nucleic acids, proteins, amino acids, peptides, antibodies, antigens,fatty acids, lipids, steroids, neurotransmitters, inorganic ions, pHlevels, free radicals, carbohydrates, chemicals, small molecules, cells,tissue, pharmaceuticals, toxins, metabolites, or physiological levelsmacroscopically, microscopically, or nanoscopically.

Controller 112 uses network 103 to couple components for signal or datacommunication. Network 103 communicates data electronically tosystems-biology platform 104. Controller 112 may be implemented usingpersonal, desktop, server, notebook, mainframe, wireless portable orother computer or processing device having processor, digital memory andnetwork or user interface.

Systems-biology platform 104 uses computer equipment, software programsor reconfigurable firmware or emulation logic devices to verify, model,simulate, or analyze stored or raw data using computational biology,such as bioinformatics, proteomics, metabolomics, pharmacogenomics orother analysis software or hardware tools. Systems-biology platform 104interprets or integrates data from biosensor platform 102, and analyzesorganism preferably as a whole on system level. Systems-biology platform104 may be integrated within one or more integrated circuit, module orprocessor; or bilaterally communicate to outside non-host signal sourcethrough wireless communication unit 106.

Controller 112 may use data storage 105 for storing processed data orapplications programs from systems-biology platform 104. Controller 112includes wireless communication unit 106, allowing bilateralcommunication with outside source, which may access or control sensorunit 111, controller 112, or therapeutic unit 113 through wirelesscommunication unit 106.

Network 103 may couple therapeutic unit 113 with controller unit 112.Therapeutic unit 113 includes therapeutic manufacture 108 for providingpharmaceuticals, biopharmaceuticals, bio-catalytic chips or devices,tissue, or physiological treatments. Biopharmaceuticals includebiological material for therapeutic use.

Therapeutic unit 113 includes therapeutic reservoir 109, which providesmicro or nano-scale reservoirs containing pharmaceuticals orbiopharmaceuticals. Contents of therapeutic reservoirs 109 may beprovided or configured before sensor 100 is implanted in or attached toorganism, or may be manufactured and filled in vivo by therapeuticmanufacture 108. Therapeutic reservoirs 109 may release or dispensecontents when appropriately signaled by network 103.

Therapeutic unit 113 includes sensor manufacture 110 unit, which mayprovide additional sensors in vivo for additional targeted sensing ormonitoring. Sensors from sensor manufacture 110 are part of or comprisebiosensor platform 102.

FIG. 1 b shows positioning chip 101 for immobilizing or positioningtarget or tissue samples on or in sensor 102 for bio-sensing asdescribed herein. Positioning chip 101 may use micro-fabrication,micro-fluidics, or microbiology to manipulate, sort, or prepare samples,reagents, or other biological entities for analysis, high-throughputassays, or diagnostic applications. Positioning chip 101 may accomplishsample placement using multi-channel patch clamp electrophysiology chipto control individual cells by applying current to cell ion channels,positioning cells onto planar patch clamp, for example, e.g., AvivaBioscience technique. The cell is sealed on-chip and analyzed or broken,and intracellular materials extracted and analyzed; if the cell is notanalyzed, cellular material may be positioned for analysis by diffusion,other natural technique, or through micro-fluidic manipulation.

Optionally positioning chip 101 comprises microelectronic array ormicrofluidic assay, including electrodes or biosensors in which at leastone microelectrode or sensor cavity or element is capable of generatingcontrollable electric current or voltage for drawing probes, samples, orreagents to locations on sensor platform 102, allowing faster,controlled hybridization or analysis.

Positioning chip 101 may use micro or nano-chips with nanoscale channelsor membranes, e.g., iMEDD NanoPORE membranes. Depending on size of suchmembranes, pores selectively exclude antibodies or proteins, whileallowing free exchange of glucose, nutrients, insulin, or othermolecules. Positioning chip 101 may position mammalian cells of hostorganism, as well as bacterial, fungal, protozoan, or other unicellularor multi-cellular organisms for analysis.

Additionally positioning chip 101 may detect or collect micro-metastatictumor cells circulating in the blood stream or other body fluids,including but not limited to nipple aspirate, cerebrospinal fluid,peritoneal wash, sputum or excrement such as urine and stool. Preferablyenrichment of tumor cells from blood stream may occur in miniaturized ormicroelectromechanical (MEMs) version of device such as autoMACS tocollect circulating carcinoma cells from blood of patients with urologiccancers, or similarly using nanoparticles conjugated with antibody toEpithelial Cell Adhesion Molecule to enrich for circulating tumor cells(CTC) of epithelial origin.

Further using positioning chip 101 in detection or collection,circulating prostate cancer cells in peripheral blood may be enriched,e.g., using technique by OncoQuick in Greiner, Germany, by usinganti-human epithelial antigen paramagnetic microbeads or enrichment fordisseminated breast cancer cells using advanced density gradientcentrifugation; circulating endothelial cells serve as marker for vesselformation and vascular damage in cancer patients, such circulating cellsbeing detectable for collection from peripheral blood usingimmunomagnetic beads coupled to antiCD146, an antibody raised againsthuman umbilical vein endothelial cells.

Preferably collected tumor cells are analyzed on biosensor platform 102;for example, disseminated breast tumor cells may be analyzed bymultiplex real-time RT-PCR (reverse transcriptase polymerase chainreaction) for mammoglobin, gabaII, B305D-C and B726P, or polymorphismsin carcinogen detoxifying UDP-glucuronosyl transferase UGT1A7 in bloodof patients with cancer of proximal digestive tract. Also enriched,using anti-epithelial cells antibody Ber-EP4, e.g., Dynal Corporationtechnique, epithelial cells derived from peripheral blood of prostatecancer patients can be analyzed using nested RT-PCR-PSA (reversetranscriptase polymerase chain reaction prostate specific antigen) assayas sensor mechanism.

Biosensor platform 102 may employ twenty-five epithelial tumor cells inbone marrow and lymph nodes of esophageal carcinoma (pT1-3, pN0-1 andpM0) patients collected, using cytokeratin and EpCAM antibodies,respectively, by positioning chip 101 for micromanipulation in biosensorplatform 102. Further DNA amplified by DNA sensor 201 using Mse-adapterPCR method may be analyzed by comparative genomic hybridization (CGH)for DNA-gains, -losses and point mutations by single-strand conformationpolymorphism (SSCP). Also total RNA isolated PBMC in peripheral blood ofbreast cancer patients, may be subject to RT-PCR luminometrichybridization assay for presence of human telomerase reversetranscriptase, which is highly expressed in majority of tumor cells.

During sensing operation, positioning chip 101 may place samples onbiosensor platform 102 for analysis. Biosensor platform 102 measures,detects, sequences, and other biological activities in serial orparallel in or out of organism. Biosensor platform 102 may usemulti-functional high-throughput and density biochip having micro ornanoarrays, having substrates manufactured using glass, nylon, silicon,ceramic, metal, gel, membranes, synthesized nanomaterials, or othermaterial.

Biosensor platform 102 provides data gathered from sensor arrays tonetwork 103, which provides data to systems-biology platform 104, wheredata is integrated or processed. Systems-biology platform 104 mayanalyze empirically-sensed and simulated factors of individual organismin combination, to determine or confirm host profile of personalbiological processes or makeup.

Systems-biology platform 104 may convey processed information to network103. Network 103 communicates processed data to components coupled tonetwork 103, including data storage 105, wireless communication unit106, therapeutic manufacture 108, therapeutic reservoirs 109, or sensormanufacture 110.

Data storage 105 keeps records or stores processed data bysystems-biology platform 104. Processed data from systems-biologyplatform 104, through network 103, optionally may be conveyed towireless communication unit 106. Wireless communication unit 106provides processed data access to external source, such as GlobalPositioning Satellite (GPS) receiver unit, media repository, personalcomputer (PC) or workstation, laptop, handheld computing device,cellular device, internal or external camera, another internalimplantable or attached sensor or chip, external biological monitoringdevice, outside network, healthcare provider, pharmacist, insuranceagent, or other device or service communicating with bio-sensor.

Processed data from systems-biology platform 104, through network 103,may be conveyed to therapeutic manufacture 108, where therapies aremanufactured according to host biological status or simulation output.Effectiveness or side-effects of therapies, produced by therapeuticmanufacture 108, are monitored by biosensor platform 102. Ongoing orintermittent feedback from biosensor platform 102, through network 103,to therapeutic manufacture 108 provides automated or iterativetherapeutic process.

Optionally therapeutic manufacture 108 stores biological therapies intherapeutic reservoirs 109. Therapeutic manufacture 108 or therapeuticreservoirs 109 communicate through network 103 for filling ordispensing. Processed data from systems-biology platform 104, throughnetwork 103, may be conveyed to therapeutic reservoirs 109, whererespective therapies are released according to biological status.Effectiveness or side effects of therapies, released by therapeuticreservoirs 109, are monitored by biosensor platform 102. For example,biosensor platform 102 may sense therapeutic effectiveness or sideeffects, while systems-biology platform 104 analyzes negative orpositive effects to make recommendations. Ongoing feedback frombiosensor platform 102, through network 103, to therapeutic reservoirs109 provides automated or iterative therapeutic cycle.

Processed data from systems-biology platform 104, through network 103,optionally is conveyed to sensor manufacture 110. Sensor manufacture 110comprises hardware or software-programmable (reconfigurable andsoftware-programmable terms may be used interchangeably) biosensors invivo that integrate into biosensor platform 102 for supplementarysensing. Sensor manufacture 110 may be used to monitor additionalbiological materials originally part of biosensor platform 102, as wellas used functionally to replace damaged sensors. Sensor manufacture 110may be used to sense newly-calculated operational conditions bysystems-biology platform 104. Optionally sensor manufacture 110 maymonitor interactions between novel drug therapies, produced bytherapeutic manufacture 108, and organism biology.

Appropriate timing of functions is preprogrammed before biosensor 100 isattached or implanted into organism. Time intervals for sensing areprogrammed according to external diagnosis, which can range fromseconds, minutes, hours, weeks, or longer. Once initial sensing begins,timing adjusts based on processed information by systems-biologyplatform 104. For example if genetic mutations within genome are foundto be rare within multiplying cells, systems-biology platform 104instructs biosensor platform 102 not to monitor genome as frequently.

Conversely if sensed or simulation parameter, input vector, stimulus,condition, environment or other host biological factor is changingfrequently, or there is a high risk of change, then systems-biologyplatform 104 instructs biosensor platform 102 to increase frequency ofparticular sensor or assay. For example if organism changes throughorgan transplant, or is infected with new virus, systems-biologyplatform 104 instructs biosensor platform 102 to increase the monitorfrequency of antigen or antibody responses while decreasing such factorsthat are relatively stable.

FIG. 2 shows biosensor platform 102 with multifunctional array 200coupled to detection system 230, and integrated sensor and detectionsystem 231. Multifunctional array 200 serves as programmable or logicalinterconnect for coupling or switching various sensor devices, andinteracts with samples and detection system 230 interprets samples intodata to be analyzed by systems-biology platform 104. Multifunctionalarray 200 may include micro and nanoarrays (M/N arrays) and biochips totest or monitor biological functions in particular organism.

Sensor components may include deoxyribonucleic acid (DNA) sensor 201,ribonucleic acid (RNA) sensor 202, peptide or protein sensor 203,antibody sensor 204, antigen sensor 205, tissue factor sensor 206,vector and virus vector sensor 207, lipid and fatty acid sensor 208,steroid sensor 209, neurotransmitter sensor 210, inorganic ion andelectrochemical sensor 211, pH sensor 212, free radical sensor 213,carbohydrate sensor 214, neural sensor 215, chemical sensor 216, smallmolecule sensor 217, exon sensor 218, metabolite sensor 219,intermediates sensor 220, chromosome sensor 221, or cell sensor 222. M/Narrays are arranged architecturally as micro-electromechanical system(MEM) or as nano-electromechanical system (NEMS). This miniaturizedarchitecture, as MEMS or NEMS device, allows multiple M/N arrays in acondensed form.

DNA sensor 201 is used to detect presence and/or sequence and/orstructure of any DNA molecules including profiling for changes inmethylation, monitor gene expression, undergo gene and DNA mapping,library screening, functional screen assays for nonsense and frame-shiftmutations, scan the whole genome including micro-array-based comparativegenomic hybridization to measure and map DNA copy number aberrations,detect disease markers, genotype single nucleotide polymorphisms (SNPs)including loss of heterozygosity analysis using SNP array hybridizationand single-strand conformation polymorphism (SSCP), genotype organisms,examine protein-DNA interactions, and determine genetic characteristicsindividual to the organism.

DNA sensor 201 utilizes high-throughput M/N arrays for hybridization anduse biochips, such as oligonucleotide M/N arrays, antibody M/N arrays,P1-based artificial chromosome (PAC) M/N arrays, bacterial artificialchromosome (BAC) M/N arrays, yeast artificial chromosome (YAC) M/Narrays, cosmid M/N arrays, cDNA M/N arrays, gene M/N arrays,whole-genome M/N arrays, SNP M/N arrays, gridded cDNA M/N arrays,Southern Blots, theme M/N arrays (array centered around a particulardisease or gene family), bead M/N arrays (arrays made up of small beadscontaining capture oligonucleotides), bead based M/N arrays (arrays inwhich reactions take place on the surface of microbeads), gel-pad M/Narrays (arrays in which chemical and enzymatic reactions can be carriedout on three dimensional pads, like miniature test tubes),microcantilever arrays (in which specific biomolecular interactionsoccur on one surface of a cantilever beam, such as changes inintermolecular interactions that generate sufficient surface stress tobend beam for optical detection, M/N gel electrophoresis chips and M/Narrays 2D gel electrophoresis chips, chromatographic protein M/N arrays,e.g., Ciphergen protein sensor, and hybridization techniques fordeoxyribonucleic acid sensing. Phenotypic markers for DNA damage orrepair include single-cell gel electrophoresis use comet assay in whichDNA damage is visualized, e.g., Komet 4.0 by (Kinetic Imaging Ltd)Imaging System.

Optionally for single nucleotide polymorphism (SNP) detection, DNAsensor 201 may apply so-called invader platform, or other device forgenetic sequencing of an individual. DNA sensor 201 can analyzeperitoneal fluid from patients with ovarian cancer for loss ofheterozygosity (LOH) at chromosomal arms 13 q (BRCA2 locus), 17 (BRCA1and p53 loci) and 22q and for mutations in their p53 and k-ras genes. Itcan detect SNP (936 C>T) in 3′ UTR of vascular endothelial growth factorgene (VEGF) in DNA extracted from blood of patients with breast cancer.

Further DNA sensor 201 can identify polymorphisms in carcinogendetoxifying UDP-glucuronosyl transferase UGT1A7 in blood of patientswith cancer of the proximal digestive tract. Also methylationabnormalities in the promoter CpG islands of p16, HOX A9, MAGE A1 andMAGE B2 can be detected in sputum of lung cancer patients with DNAsensor 201. Sharply-elevated levels of stool DNA can be detected by DNAsensor 201 in patients with colorectal cancer. Stool DNA of surfaceepithelial cells is quantified using Picogreen fluorimetry.

DNA sensor 201 can detect chromosomal aneuploidy in cervicalintraepithelial neoplasia or dysplasia using interphase cytogenetictechnique called dual-color fluorescence in situ hybridization (FISH)targeting chromosomes 1, 7, 9 and 17 in Pap-smear slides and a thinlayer of cervical cells.

Using DNA sensor 201, nipple aspirate fluid (NAF) containing epithelialcells shed from the breast ductal system can be analyzed. Extracted NAFDNA can be PCR amplified and analyzed for loss of heterozygosity innuclear genome and deletions in mitochondrial genome using microsatelitemarkers and primer pairs, respectively.

Further DNA sensor 201 can be used to detect acute lymphoblasticleukemia prenatally by analyzing fetus blood to detect TEL-AML1 by FISHand genomic breakpoints by long-distance PCR. Using DNA sensor 201 andgenomic DNA from whole blood, germ line polymorphism in KLK10 at codon50 (GCC to TCC) associated with risk of occurrence in prostate cancercan be detected.

Also using DNA sensor 201, epigenetic changes, such as changes in GSTP1methylation associated with prostate cancer can be detected in bodilyfluids, e.g., urine and plasma, of prostate cancer patients. Thisdetection uses real-time quantitative MSP and conventional MSP.

Further DNA sensor 201 is used to search for pieces of DNA in blood thatare abnormally long, which is a signature of dying cancer cells; thistest can be used for early diagnosis for patients with gynecologic andbreast cancers. Optionally oligonucleotide array-based genotypingplatform, such as Perlegen, is used for accelerated SNP analysis,allowing whole-genome scanning by DNA sensor 201.

RNA sensor 202 may be used to detect presence, sequence or structure ofRNA molecules, such as spliced and un-spliced RNA, mRNA, tRNA, rRNA,improperly transcribed RNA, properly transcribed RNA from diseased DNAsources, ribozymes, RNAi mechanism and application in relation to cancertherapy, or changes or differences in mRNA levels, or structures made ofribonucleic acids. RNA sensor 202 utilizes high-throughput M/N arraysfor hybridization techniques, inclusive of DNA sensor 201. Probes may bemade to hybridize with RNA molecules, and Northern blot may be used inplace of Southern blot technique.

RNA from enriched epithelial cells using anti-epithelial cells antibodyBer-EP4, e.g., per technique by Dynal Corporation, derived fromperipheral blood of prostate cancer patients is analyzed for usingnested RT-PCR-PSA assay by RNA sensor 202. Further, RNA sensor 202 canbe used instead of second-look laparotomy in women with ovariancarcinoma treated with surgery and chemotherapy and show no sign ofdisease. Processed peritoneal washings are analyzed by telomerase repeatamplification protocol (TRAP) assay to detect residual disease. TotalRNA isolated PBMC in peripheral blood of breast cancer patients,subjected to RT-PCR luminometric hybridization assay for presence ofhuman telomerase reverse transcriptase that is highly expressed inmajority of tumor cells.

Peptide or protein sensor 203 is used to detect primary, secondary,tertiary, or quaternary structures or activity of amino acid-basedstructures, such as sequence, enzymatic activity, protein function,interactions with agonists and antagonists, interactions with organic orinorganic structures or molecules, interactions with membranes, foldingand enzymatic changes resulting in external factor, such as temperature,pH, ion concentrations, etc., N or C terminal characteristics, prionsand misfolded proteins, amount and concentrations of proteins, bound andunbound state of proteins, sub-cellular localization, phosphorylated anddephosphorylated states, stages of degradation by proteases, stages oftranslation, gene and protein expression levels, e.g., using techniquessuch as ANTIBIOMIX (Milagen, Inc.) or Antigen Retrieval (BiogenexLaboratories, Inc.), protein-protein interactions, protein-smallmolecule interactions, protein-antibody interactions, protein mutationsdue to transcription and translation mistakes, or measurable factorsassociated with amino acid based structures. Sensor 203 may beimplemented using electrophoresis tag or microassay to identify proteinor gene simultaneously, e.g., Aclara eTag assay (Mountain View, Calif.).

Peptide or protein sensor 203 utilizes high-throughput M/N arrays forhybridization and use biochips, such as protein M/N arrays, proteome M/Narrays, whole-proteome M/N arrays, electrospray fabricated protein M/Narrays, gene expression M/N arrays, reverse transfection M/N arrays (forexample membrane proteins that are difficult to purify), functionalprotein M/N arrays, Western blotting, microcantilever arrays, orquantitative and qualitative high-throughput techniques for amino acidentities.

Peptide or protein sensor 203 can be used to detect proteins incerebrospinal fluid of patients with primary brain tumors.Differentially-expressed proteins in processed CSF are digested andpeptides identified by mass spectrometry. Presence of tumor-relatedproteins such as VEGF and VAV signifies presence of a primary braintumor (179). Sensor 203, like SELDI protein-chip, similarly may be usedto identify sixteen protein biomarkers in urine of bladder cancerpatients, or instead of second look laprotomy in women with ovariancarcinoma who have been treated with surgery and chemotherapy and showno signs of disease. Processed peritoneal washings may be analyzed fortelomerase activity to detect for residual disease.

Protein or peptide sensor 203 may be used in detection of diminishedlevels of N-CAM of <130 kDa in human serum of patients with brain tumorsand the 80 kDa form associated with glioma. Further, protein and peptidesensor can be used in diagnosis of breast cancer by analysis of nippleaspirate fluid (NAF). Using SELDI-TOF capability, the presence ofpeptides at 4233.0 Da and 9470.0 Da is associated with cancer and thepresence of 3415.6 Da and 4149.7 Da may be expected for normal samples.Thus sensor 203 can differentiate between diseased and unaffectedpopulations.

Similarly protein sensor 203 may be used in breast-cancer diagnosis byanalysis of serum samples. Samples applied to metal affinity capturechips activated with Ni²⁺. Using SELDI protein chips/mass spectrometryfeature and software to detect selected discriminatory peaks separatecancer from non-cancer groups.

Using same features of sensor 203, serum is analyzed to differentiatebetween hepatocellular carcinoma (HCC) and chronic liver disease (CLD),where α-fetoprotein fails as biomarker. Detecting 151 potentialbiomarkers in this way, system can provide diagnosis method for HCC.Using protein sensor 203 in diagnosis of prostate cancer, protein of50.8 kDa can be detected in serum even where PSA levels are <4 ng/mL.

Further protein sensor 203 may be used in diagnosis of colorectal cancerdetecting elevated HER-2 levels using standard ELISA andimmuno-histo-chemistry (IHC) techniques. Elevated levels of secretedurokinase-type plasminogen activator (uPA) can be detected by sensor 203in serum for diagnosis of pancreatic cancer using sandwich ELISA orsimilarly, elevated levels of kallikrein 10 in serum for diagnosis ofovarian cancer, or elevated levels of basic fibroblast growth factor(bFGF) in nipple aspirate fluid in diagnosis of breast cancer, orelevated levels of fibroblast growth factor-2 and pleiotropin in serumfor testicular cancer diagnosis or interleukin 6 in the serum ofhormone-refractory breast cancer patients using immunoassay.

Antibody sensor 204 may be used to detect monoclonal or polyclonalantibodies. Similar to above sensors, hybridization with M/N arrays maybe used. Probes may be chemical or molecular biological material thathybridize to targeted antibody, such as DNA, RNA, peptide, protein,small molecule, steroid, or lipid. Microcantilever arrays and otherbinding techniques can be applied.

Antibody sensor 204 may use so-called phagotope biochip to display phagewith epitopes that react with antibodies in sera of patients withovarian cancer, or other cancers. Also presence of elevated levels ofanti-survivine autoantibody in serum of head or neck cancer patients isdetected by antibody sensor 204 using recombinant protein survivine asantigen.

Antigen sensor 205 may be used to detect or recognize individual immuneresponse factors. For example antigen sensing may detect autoimmuneresponse factors, such as sensing multiplex character autoantibodyresponse in systemic lupus erythematosus, rheumatoid arthritis, ormultiple sclerosis. Another example of antigen sensor 205 applicationmay be identification or targeting of cell surface antigens for cancertherapy, e.g., Genentech approach.

Antigen sensor 205 may be used for early diagnosis of lung cancer orefficacy of chemotherapy by detecting nucleosomes in serum using assay,e.g., Cell Death Detection ELISAplus (Roche Diagnostics). Furtherantigen sensor 205 may detect tumor-associated antigens such asCYFRA21-1 for non-small cell lung cancer, and CEA, NSE and ProGRP forsmall-cell lung cancer.

Other sensing techniques for cancer detection contemplated hereininclude anti-malignin antibody screen test and tests for cancer markersincluding alpha fetoprotein (AFP), CA 15.3, CA 19.9, CA125,carcinoembryonic antigen (CEA), EVP test for epstein bar virus, T/TnAntigen test, TK-1 test and prostate specific antigen (PSA) or free PSA(fPSA) test. For bladder-cancer bladder-tumor-associated antigen test(BTA), BTA stat test, BTA TRAK test, fibrin/fibrinogen degradationproducts test (FDP), and NMP22 assay. Protein-based markers mayilluminate and map abnormal cells, e.g., Inpath system. Other bloodtests include CBC blood test, biological terrain assessment (BTA),Pre-Gen 26, telomerase test or DR-70 test.

Tissue-factor sensor 206 may use tissue factor M/N array to sensetissues, tissue factors, or tissue origin, using probes or antibodies tohybridize with targets. Tissue-factor sensor 206 may detect increase inprostaglandin E₂ production in cells that over-express COX2. Thisdetection is associated with enhanced growth, migration and invasion asin bladder tumors.

Lipid or fatty acid sensor 208 may provide membrane mapping, M/N gelelectrophoresis chips and M/N arrays 2D gel electrophoresis chips,detergent analysis, M/N array analysis of glycolipids and membraneproteins, membrane fluidity analysis, cholesterol analysis, or othertest to examine cellular or intracellular organelles lipid bilayers.

Lipid or fatty acid sensor 208 may detect changes in exposed membrane;for example, such sensor 208 may produce antibody, with traceable labelconjugated thereto, to anionic phospholipids (AP), such asphosphatidylserine, phosphatidylinositol and phosphatidic acid, that aremore specific for AP than annexin V. When released into blood streamthis antibody binds activated, by inflammatory cytokines, hypoxia,hydrogen peroxide, thrombin or acidic conditions, endothelial cells andthus, tumor blood vessels have increased exposure of anionicphospholipids on their surface. Localization of label enableslocalization of tumor.

Lipid or fatty acid sensor 208 may detect levels of accumulation ofsynthetic membrane-permeable alkyl-lysophospholipids (ALPs), such asEdelfosine, Mitelfosine and Perifosine, that are anticancer agents thatinterfere with lipid mediated signal transduction.

Vector or virus vector sensor 207 may use microarray or assay with knownsequenced virus attached, e.g., DeRisi Laboratory. Unknown viruses maybe detected through examining homology to known viruses, and subsequentarrays can be manufactured by sensor manufacture 110 to detect newviruses. Optionally assays that detect homologs can be applied, such asCelera Diagnostic Viroseq™ HIV system for detection of mutations inhuman immunodeficiency virus (HIV) genome that confer drug resistance.Optionally assays for virus RNA can be used, such as Bayer DiagnosticVersant® HIV-I RNA 3.0 Assay for qualification of HIV-I RNA in plasma ofinfected people.

Further microparticle enzyme immunoassay (AxSYM HbsAg V2), e.g., AbbottLaboratories, may be used in quantifying reactivation of HBV duringchemotherapy for lymphoma with Doxorubicin along with real-timequantitative PCR specific to region of major S protein. Virus and virusvector sensor 207 may be used for detection of oncolytic virusreplication in tumor tissues.

Steroid sensor 209 detects levels of steroids in the body, and monitorsor controls levels of steroid hormones. Sensor 209 targets hormonalchanges associated with puberty, menopause as well as fitness-conscioussteroid-pumping athletic types.

Neurotransmitter sensor 210, small molecule sensor 217, or exons sensor218 detects using M/N arrays, such specific antibodies as probes thathybridize with desired targets. Inorganic ion or electrochemical sensor211 may detect ionic concentrations using techniques, using MEMStechnologies with dielectric currents, microfluidics, or dialysis on aN/M platform. pH sensor 212 may read pH by detecting H₃O⁺ concentrationslike silicon oxide pH sensors, e.g., Intelligent Pill. Free radicalsensor 213 may be used to measure free radical activity, by usingantioxidants as probes.

Carbohydrate sensor 214 may use oligosaccharide arrays, polysaccharidearrays, or carbohydrate chips, e.g., Glycominds glycochip, to measureglycan-protein interactions such as enzymes, antibodies, and lectins.Branched carbohydrates may bind to lectins involved in cell adhesion andmigration processes. Also, natural branched carbohydrate like Lewis y,which is over-expressed in, for example, colon and ovarian cancer may bedetected by carbohydrate sensor 214. Such sensor 214 may apply to wholeblood glucose (WBG) monitoring system, or continuous glucose monitor,e.g., Sensors for Medicine Science.

Neural sensor 215 measures action potentials or voltage between neuronsin central nervous system, using thin-film M/N electrodes as front-endsensors in MEMS and NEMS.

Chemical sensor 216 senses native or foreign chemicals, such as toxins,pharmaceuticals, vitamins, minerals, or other organic or inorganicchemicals. Chemical M/N arrays may be used, in which arrays of smallorganic compounds may be used to analyze interactions of proteins withvarious compounds. Conversely proteins or RNA may be used as probes todetect chemical substances.

Chemical sensor 216 may measure levels of carcinogen, benzo(α)pyrenediol epoxide, a metabolic product of benzo(α)pyrene found in tobaccosmoke, known to cause 9p21 aberrations in peripheral blood lymphocytesin bladder cancer cases. Further chemical sensor 216 may measuretobacco-specific carcinogen4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) that can inducetransformation of human breast epithelial cells, and may be directlyrelated to initiation of human breast cancer in smokers.

Metabolites sensor 219 uses protein or antibody M/N arrays thathybridize to particular metabolites. Sensor 219 is useful to detectexcessive buildup of metabolites. For example metabolites sensor 219 canmeasure serum homocysteine levels, associated with increased risk ofcervical cancer, and further DNA sensor 201 may detect commonpolymorphisms in one-carbon metabolic pathway; examples of suchmutations include MTHFR C677T, MTHFR A1298C and MTR A2756G. Increasingcopies of MTHFR 677 variant polymorphism is associated with increasedhomocysteine levels whereas increasing copies of MTR 2756 variantpolymorphism is associated with decreased levels of such metabolite.

Intermediates sensor 220 uses various protein and antibody M/N arraysthat hybridize to particular intermediates. Sensor 220 is useful todetect excessive buildup of intermediates; also sensing specificsequence, tertiary or quaternary structure of intermediates is used indrug design specificity.

Chromosome sensor 221 senses abnormalities in folding of chromosome,such as faulty histones, senescence-associated heterochromatic foci, orSAHF, since genes contained in these chromosomal regions are switched-onin proliferating cells, but are switched-off or “silenced” duringcellular senescence.

Cell sensor 222 attaches whole living cells as probes, and is used forinteractions with whole cells, such as cytotoxicity, drug metabolism,pharmacokinetics, target validation, interactions with other cells,extracellular materials, phenotypic analysis of genes and interferingRNA, as well as other biomolecules and compounds, e.g., Excellin LifeScience bionic chip, which provides cell growth on chip. Effectively thecell becomes part of the chip, which allows manipulation and analysis ofcell using microelectronics; the chip sends electrical signals throughan on-board living cell, which detects changes in cell-membranestructure. The bionic chip can monitor and detect conditions that cancause cellular damage.

Optionally image cytometric measurement of breast fine needle aspiratescan be used in cell sensing to predict nodal involvement in breastcancer. DNA ploidy, S-phase fraction, G0G1/G2M ratio, and minimum(start) and maximum (end) nuclear pleomorphism indices (NPI). Furthercytometric imaging allows differentiation between normal cells in whichPML protein resides in discrete PML bodies and promyelocytic leukemiccells in which PML protein is genetically rearranged or dispersedthroughout the nucleus.

Sensor unit 111 may measure or transmit blood pressure, flow rate orother sensor data wirelessly to controller unit 112, similarly toso-called cardioMEMS devices for monitoring pressure within aorticaneurysm. Biosensor 100 is implanted using catheter and transmits datato controller unit 112. Optionally such device can be used assessingcirculation to organ after transplant or reconstructive surgery. Thisprovides physician with early indication of whether surgery issuccessful and prevent irreversible damage to organ.

Biosensor 100 may use implantable blood-flow monitoring system forproviding synchronized blood vessel flow or myocardial wallcontractility data to external monitor independent of transcutaneousleads. Further, since heart failure (HF) status of a patient isdetermined based on morphology of signal representative of arterialpulse pressure, the signal can be plethysmography signal that isproduced by implantable or non-implanted sensor.

Time-derivative sensed signal may be produced based on signalrepresentative of arterial pulse pressure; time derivation signal can beused to determine maximum and minimum peaks of signal representative ofarterial pulse pressure. HF status can be assessed directly fromtime-derivative signal.

Biosensor 100 can be implanted using placement catheter, endoscope, orlaparoscope; such device can be secured in LV or heart wall, e.g., usingcorkscrew, helical anchor, harpoon, threaded member, hook, barb,fastener, suture, or mesh or coating for receiving fibrous tissuegrowth.

Biosensor 100 provides less-invasive chronic measurement of leftventricular blood pressure or other parameters. Biosensor 100 canperform cardiosaver function to indicate to human subject thatmyocardial infarction is occurring; data is transmitted wirelessly tocontroller unit 112 for systems-biology analysis. Therapeutic reservoir109 can inject thrombolytic or anti-thrombogenic agent into bloodstreampromptly to dissolve thrombus that caused myocardial infarction, andprevent formation of additional thrombi.

Biosensor 100 may sense impedance measurements of heart, respiratory orpatient motion, and from these measurements, generating alarm signalwhen measurements indicate occurrence of cardiac arrhythmia. Optionallyrate-responsive pacing system includes sensor of minimum oxygen contentin right atrium over prescribed time interval, and using such minimumoxygen content as control parameter for adjusting rate of pacemaker.

Optionally for sensors in multi-functional array 200, nano-particlesthat specifically bind to particular molecules can be used to detectsequence, folding, binding, interactions, function, or overallcharacteristics. Once bound to particular biological molecules,arrangement of distances between nanoparticles results in differentobservable properties, such as color or pattern.

Array 200 may be configured electronically by systems biology platform104 to couple or interconnect selectively according to simulation ormodeling to access actual host condition via one or more biosensorsignals. Such sensed signal set may be compared by simulator againstmodel or other software prediction to confirm host or target materialhealth or other problem, as described herein.

Nanoparticle arrangement on biological molecules provide or indicatefunction, e.g., Northwestern University DNA-Driven Assembly ofBiomaterials system. By attaching gold particles to DNA nucleotides, DNAhybridizes with complementary strand and creates specific arrangement ofgold particles. That arrangement of nanoparticles gives detectable coloror pattern, which can be detected by optical device, and DNA can besequenced.

Measuring color differences between nano-particle arrangement can alsobe applied to other biological molecule, e.g., Northwestern UniversityNanoscale Bioassay for Specific Antibodies. Rather than engineeringnanoparticles that attach directly to the biological molecule,nanoparticles can be attached to specific antibodies. Binding ofantibodies to targeted protein, DNA sequence, small particle, lipid,chemical, or other biological produces a particular color that isdetectable or analyzable.

Also Nanoplex Technologies Nanobarcode Particles, made of differentmetals attached to biological molecules for multiplexing bioassays useprobes attached to alternating metals on Nanobarcode to hybridize withbiological molecules; then current can be run through Nanobarcode todetermine molecules that bind to probes.

Detection system 230 may produce data from hybridization M/N arrays andother analysis techniques, e.g., fluorescent scanners, laser scanningphosphorimagers, mass spectrometry, fiber optics, atomic forcemicroscopy, parallel surface plasmon resonance imaging (allows directanalysis of binding events without need of reporter systems or tags),conclusive-induced dissociation (CID) mass spectra through electrosprayionization tandem mass spectrometry (ESI-MS) on triple or quadruple orion trap mass spectrometers, real-time polymerase chain reaction (PCR),PCR, Fluoresecence in situ Hybridization (FISH), or charged coupleddevices (CCDs).

Integrated sensor or detection system 231 may produce data from samples,without separating detection from hybridization or other technique.Optionally semiconductor-based M/N array can be used, e.g., CombiMatrixmatriXarray; such array allows precise, digital control ofelectrochemical detritylation, including embedded sensor designed insemiconductor substrate, alternatively to conventional fluorescencetechnology. Hybridization with array sends direct electronic signals foranalysis.

Another example of integrated sensor detection system 231 assay, can beGeneFluidics 3D micro-fabricated platform with embedded electrochemicalsensor array. This platform conducts molecular analysis of raw DNA orprotein samples, e.g., no PCR or immunoassays. Electrochemical detectionof samples, such as whole blood, saliva, stomach acids, or other bodilyfluids, uses current to measure electron transfer with current signal,associated with hybridized nanomolecules, e.g., ssDNA, hybridizablenanoparticles).

Biosensor 100 generally comprises biological microelectromechanical(bio-MEMs) sensor chip or detection or transducer device that may beimplemented or computer-modeled for operation in silicon, silica, glass,polymer or other substrate or instrumentation cavity, beam, surface,channel, encapsulated molecules, membrane, quantum dot or nanocrystal(e.g., CdS, CdSe, CdTe, ZnSe, or other colloidal group II-VIsemiconductor), matrix or array for single or multi-channel independentsignal detection in two or three dimensions in vitro or in vivo.

For example, sensor 100 may serve as high-throughput and sensitivitybio-physical, pharmaceutical or chemical recognition probe or cartridgefor identification and/or characterization of host tissue or serum DNA,RNA, nucleic acids, protein, lipids, carbohydrates, enzymes, aptamers orother biomolecular or signal reporter target or any interaction,mutation, mass or rate thereof. Also such sensor may provide integrated,monolithic, discrete, or distributed, reagent-based or reagantless,microfluidic lab-on-chip microbiology mass spectrometry, flowimmunosensor (e.g., FAST monitor for food or water quality), microarrayor microassay functions, such as growing virus, bacteria or othereukaryotic or prokaryotic cells in microcells, nucleotide hybridization,polymerase chain reaction, molecular imprinting, chemical synthesis,ligand fishing, phage selection and concentration, multicomplexformation, diffusion limited concentration, or challenging antibioticsfor rapid target detection, antibody susceptibility determination, oraffinity and kinetic analysis.

Biosensor 100 may be implemented in quartz crystal microbalance fordetecting or monitoring physical or chemical associated mass change ordissipation rate. Also whole cell or host sensor detection method maysense radioisotope, fluorescence, colorimetric, electrochemical,chemiluminescence, or bioluminescence. Additionally molecular orlipid-layer membrane-based sensor may operate to report change inelectrical ionic, e.g., Ion Channel Switch biosensor using alternatingcurrent or voltage.

Furthermore encapsulated molecules may employ probes encapsulated bybiologically localized embedding (e.g., PEBBLE nanosensors forintracellular chemical sensing, which may be delivered via gene gun,picoinjection, liposomal delivery, or phagocytosis, use matrices ofcross-linked polyacrylamide, cross-linked decyl methacrylate, andsol-gel silica) for H⁺, Ca²⁺, K⁺, Na⁺, Mg²⁺, Zn²⁺, Cl⁻, NO₂ ⁻, O₂, NOand glucose detection; optionally encapsulated outer shell may bemodified as configurable platform to target selectively specificbiological locations or antibodies, such as including or excludingspecies variously reactive to passing through or filtered by the polymermembrane.

Biosensor 100 may recognize protein for antigen-antibody recognition,particularly by localizing or mapping protein residue epitopes. Forexample sensor contact at epitope-paratope interface functions viacrystallographic analysis of one or more poly- or monoclonal orantigen-antibody complex. Also sensor 100 may detect cross-reactivebinding with antiprotein antibodies using synthetic peptides asantigenic binding probe for free peptides or peptides adsorbed tosolid-phase, conjugated to carrier or attached to synthesis support.

Additionally sensor 100 may detect cross-reactive binding decrease toidentify critical residues in peptides via systematic residuereplacement, as well as other protein-protein interaction, for example,between protease-inhibitor, antibody-antigen, enzyme-inhibitor,hormone-receptor, or signal transduction or transcriptional complexes.Protein sensing analyte may include fatty acids, maltose, biotin, Ca²⁺,Co²⁺, Zn²⁺, Cu²⁺, glucose, glutamine, or other organic serum or tissuematerial.

Biosensor 100 may immobilize or control orientation of biomoleculartarget binding or catalytic sites via adsoption, entrapment behindmembrane or in polymer or sol gel, covalent coupling,surface-immobilized polymer, or other capture system. Sensor orientationcontrol may be accomplished via covalent coupling with attachedglycosides, generation of specifically-located thiol groups, use ofantibody-binding proteins, avidin/streptavidin capture system, or use oftags with engineered antibody fragments.

Additionally sensor spatial control of surface immobilization may usesoft lithography for substrate or surface patterning to introducesurface function, deposition control by physical placement,light-directed immobilization and patterning, or electro-chemicaldeposition control, for example, using elastometric polymer polydimethysiloxane PDMS.

Also molecular imprinting polymer sensor may employ affinity sensorwhere response is produced by accumulation of template on sensorsurface, receptor sensor where response is generated by change inpolymer characteristic or induced by template interaction, orenzyme-mimicking sensor where response is generated according to changein environment induced by molecular imprinting polymer-mediatedcatalytic reaction.

Furthermore antibody-based sol-gel sensor may use competitive assaydetection, where antibody is encapsulated in gel, sol-gel sensor isimmersed in sample containing analyte concentration and knownfluorescently labeled analyte solution, excess analyte is washed fromgel, and fluorescence emission from remaining bound analyte is measuredoptically; displacement assay detection, where antibody is encapsulatedin gel with pre-bound fluorescently-labeled analyte, and gel is removedfrom solution and fluorescence emission from undisplaced analyte ismeasured; and fluorescence quenching detection, where fluorescentlylabeled antibody is encapsulated in gel, which is immersed in sample,and bound analyte quences fluorescence from antibody tag.

Biosensor 100 may employ optical biosensor or transducer with variousassay formats. Direct assay may not use label, and analyte surfacebinding is measured directly. Sandwich assay secondary antibody binds tosurface-bound analyte molecule after analyte binding to sensor surface.Competitive assay enables binding-site competition on sensor surface,and low sensor signal is obtained for high analyte concentration.

Optical transducer sensor may use input grating coupler (e.g.,bidiffractive grating coupler), prism coupler, planar or nonplanar,polarimetric, ion-exchange or deposited-rib, channelized ornon-channelized waveguide or interferometer (e.g. Mach-Zehnderinterferometer), as well as surface plasmon resonance sensor (e.g.,BIACORE system) using prism coupler, resonant mirror with vibro-stirrer(e.g., Iasys), evanescent wave fiber optic biosensor for multi-analytedetection (e.g., RAPTOR antibody identication system), displacement flowdetector, or other optical or time-resolved or phase fluorescencetransducer (e.g., to detect fluorophore-labeled binding protein orfluorescence resonance energy transfer), or fiber optic elements.

Biosensor 100 may employ acoustic transducer or wave device, such asbulk or surface acoustic wave device, thickness-shear mode resonator,shear-horizontal surface acoustic wave, acoustic plate mode, or lovewave sensor, for example, to detect and characterize sensitivebiological binding events in real time without labeling, by measuringenergy loss occurring at liquid-solid biomolecular interface.

Biosensor 100 may employ fast-flow injection or microtiterplateimmunoassay using enzymatic amplification electrodes, for example, viabi-enzymatic substrate recycling for signal amplification usingelectrochemical or bioelectrocatalytic redoxlabel immunoassay.Bioelectrocatalytic sensor electrode material for detecting phenolictargets via alkaline phosphatase measurement, for example, may includeglassy carbon, graphite, carbon paste or ink, or gold.

Preferably sensing devices or techniques are provided or performed inminiaturized implantable format. However some sensor devices or methodsmay require sample from implanted device to be transferred to instrumentlocated outside the body. Data generated by such instrument istransmitted to systems-biology platform 104 for analysis or modeling.

Biosensor platform 102 sensors, detection systems, or components mayapply to parasitic or symbiotic organisms, such as bacteria, fungi,protozoa, plant, or other unicellular or multi-cellular organismsprovided in host organism. For example DNA sensor 201 may sense DNAstructure of fungus cell living within such organism, peptide or proteinsensor 202 may read its protein structures, and other sensors may readother biological properties. This information along with data from hostorganism is interpreted with systems-biology platform 104, and solutionto expunge fungi is calculated or implemented.

FIG. 3 a shows software components of systems-biology platform 104. Oncebiosensor platform 102 produces comprehensive data on system, it is sentto network 103 and processed or analyzed by systems-biology platform104.

Systems-biology platform 104 analyzes overall or partial structure ofsystem or host, combining data from sensor components as well as modeldata of biosensor platform 102. Systems-biology platform 104 usessoftware for analyzing genomics 301, proteomics 302, computationalchemistry 303, pharmacogenomics 304, computational biology 305,computational biophysics 306, computational cell behavior 307,pharmacokinetics 308, metabolomics 309, transcriptomics 310,bioinformatics 311, other computational behavior of the biologicalsystem, or other “omics” studies.

Other software may be integrated to understand or implement biologicalsystem on personalized level, e.g., specific gene sequence, individualprotein interactions, personal localized mRNA levels, dynamics ofparticular system, methods of control, personal cytotoxicity, andmethods to design and modify the system; comprehensive data set isgenerated to understand fully or partially subject organism.

Genomics 301 may map, sequence, analyze, or discover function oforganism genome. Structural or functional genomics may be used ingenomics 301. Proteomics 302 analyzes organism proteome, describing setof proteins expressed during lifetime of cell or group of cells.Proteomics 302 calculates structure determination, at lower level, tofunctional analysis, or cell modeling at higher level of modeling.

Computational chemistry 303 uses algorithmic tools to facilitatechemical analyses. Chemical analysis occurs at atomic or molecularlevel, examining how individual and groups of atoms, compounds, or otherstructures interact with living system; further it analyzes chemicalrelationships between biological structures.

Pharmacogenomics 304 calculates potential drug responses based onpersonalized genetic information. This information is useful fordetermining appropriate therapies or preventing adverse reactions.

Computational biology 305 uses algorithmic tools to facilitatebiological analyses. Computational biophysics 306 uses algorithmic toolsto facilitate biophysical or biokinetic analyses. Computational cellbehavior 307 uses algorithmic tools to facilitate complete analyses ofintracellular or intercellular behavior.

Pharmacokinetics 308 determines or predicts kinetic interactions betweenpotential drugs and organism biological molecules, taking into accountvariable interaction factors, such as sterics, charge, dipole forces, orother factors that determine molecular interactions.

Metabolomics 309 analyzes organism overall metabolic profile, such asmetabolism rates, amounts of metabolite intermediates, metabolicefficiency, structure of metabolic proteins, interactions betweenmetabolic proteins and therapies, phosphorylative rates, or otheraspects of individual metabolism.

Transcriptomics 310 analyzes organism transcription profile, such asefficiency, transcription errors to mRNA, intron-exon-splicing,biological transcription machinery, or other attributes of organismtranscription.

Bioinformatics 311 undergoes database-management activities, involvingpersistent sets of data that are maintained in consistent state overindefinite periods of time. Bioinformatics 311 provides informationcontent or flow in biological systems and processes; it serves as bridgebetween observations (i.e., data) in diverse biologically-relateddisciplines and derivations of understanding (i.e., information) abouthow systems or processes function, or subsequently the application.

FIG. 3 b shows ability to transfer information between systems-biologyplatform 104 and data storage 105 through network 103. This allowscomparative studies between previously programmed and stored data withreal-time computation; comparative studies serve as check against errorsmade by biosensor platform 102, and provide insights into overallsystems understanding.

Also data storage 105 stores information processed by systems-biologyplatform 104. Data storage 105 may be located internally or externallyrelative to the organism, which can be accessed through wirelesscommunication unit 106.

Regulation software or overlay 320 couples to data storage 105. Whensystems-biology platform 104 communicates with data storage 105,regulation overlay 320 assures that therapies, instructions, or othercommunications complies with Food and Drug Administration (FDA), Patentand Trademark Office (PTO), or other government regulatory bodies.

Regulation overlay 320 can store information or instructions for privateagreements or regulations, such as contract or licensing agreementbetween biosensor 100 and pharmaceutical company. Depending on severityof organism condition or systems-biology platform 104 suggested therapy,communication directly or indirectly with FDA may be possible ininstances where “expanded access,” “compassionate use,” “wellcharacterized biological products,” and other FDA exceptions apply. FDAmay respond favorably and allow use of unapproved therapy (suggested bysystems-biology platform 104) if exceptions apply.

Systems-biology platform 104 may implement neural network to modelbiological system or serve as decision aid for medical applications,problems or diagnosis. For example such platform 104 may employ methodsas pattern recognition, feature extraction, supervised learning,unsupervised learning, or learning algorithms. Supervised learningmethods may include Fisher's Linear Discriminant, Gradient DescentProcedures, Perceptron Algorithm, Relaxation Procedures, or PotentialFunctions for linearly separable sets, or Nonlinear DiscriminantFunctions, Hypernet, Minimum Squared Error Procedures (MSE), orHo-Kashyap Procedure for nonlinearly separable sets.

For multiple category classification problems, supervised learningmethods may include the Fisher Linear Discriminant, Kesler Construction,or Backpropagation. Unsupervised learning methods may includeclustering, Kohonen networks, Kohonen Competitive Learning, Hebbianlearning, Adaptive Resonance Theory (ART) or prototype distribution map(PDM). Clustering approaches may include Basic Isodata Procedure,similarity measure approach, or criterion functions.

Criterion functions approaches may further include sum of squared errorcriteria, minimum error criteria, or scattering criteria, and suchcriteria may be used in an iterative optimization procedure. Platform104 may also employ clustering approaches such as hierarchicalclustering or metrics.

To assist in medical decision-making, systems-biology platform 104 mayimplement artificial intelligence or decision techniques, particularlydata-based techniques or knowledge-based techniques. Data-basetechniques may include approaches such as database, decision theory,pattern recognition, or Bayesian analysis, while knowledge-basedtechniques may include mathematical modeling and simulation, symbolicreasoning, as well as databases.

Systems-biology platform 104 may employ database such as patient recordstructures (e.g., hierarchical databases, National Library of Medicine,MUMPS (Massachusetts General Hospital Utility Multi-Programming System),ARAMIS system, PROMIS (problem-oriented medical information system), ormedical database management system (e.g. MEDUS/A)). Systems platform 104may employ disease database (e.g. oncology, rheumatology), ordecision-support system (e.g. HELP program).

Platform 104 may employ differential diagnosis database (e.g. RECONSIDERor DXplain), online database, radiological database (e.g. CHORUS(collaborative Hypertext of Radiology)), or Human Genome Project.Mathematical modeling and simulation may apply to modeling of organismor biological process. Biological process may be represented bymathematical equations and evaluated.

Simulation involves representation of organism or biological process ona computer. Mathematical formulation may apply to administration ofdrugs or analysis of drug toxicity or drug level in a biological system.Pattern-recognition techniques may include discriminant analysis, methodof classification using Bayes' Rule, parameter estimation, supervisedlearning, or unsupervised learning.

Unsupervised techniques may include Parzen windworks, k-nearest neighborestimation or other learning clustering techniques. Decision theorytechniques may employ Bayesian analysis or Markovian analysis. Symbolicreasoning techniques may employ knowledge-based expert systems includingearly expert systems, second-generation expert systems. Techniques ofexpert systems may include knowledge representation, heuristic search,natural language understanding, and exact reasoning. Second-generationexpert systems may employ causal models, reasoning with uncertainty, orhybrid systems.

Systems-biology platform 104 may implement fuzzy techniques, (e.g. fuzzyset theory, fuzzy logic, fuzzy variables, or membership functions) foruse in neural networks and expert systems. In dealing with uncertaintyin supervised learning networks, neural networks may further employpre-processing of fuzzy input, propagation of results through thenetwork, or interpretation of final results.

Propagation of results may employ max-min networks, learning algorithmsfor interval data, or analogue models. Unsupervised learning methods mayemploy fuzzy associative memories or fuzzy clustering. Fuzzy methods foruse in clustering include relation criterion functions, object criterionfunctions, fuzzy isodata, convex decomposition, numerical transitive,generalized nearest neighbor rules, or HCM/FCM clustering algorithm.

Uncertain information in knowledge-based system may employ fuzzytechniques when dealing with uncertainty in relation to input data,knowledge base, inference engine (e.g., binary logic engines or fuzzylogic engines), evidential reasoning (e.g., possibility theory,probabilistic approaches, or Dempster-Shafer Belief Theory),compatibility indices, or approximate reasoning.

Alternatively systems-biology platform 104 may employ probabilisticsystems or statistical analysis for analysis of medical data.Probabilistic systems may include Bayesian approaches, parameterestimation, discriminant analysis, statistical pattern classification,unsupervised learning, or regression analysis.

Bayesian approaches may include Bayes' Rule, Bayes' Decision Theory,risk analysis, supervised Bayesian learning, or decision trees.Parameter estimation may include maximum likelihood estimation orBayesian estimation. Unsupervised learning may include Parzen windowapproach, nearest-neighbor algorithm, mixture densities and maximumlikelihood estimates, unsupervised or Bayesian learning.

For example systems-biology platform 104 receives raw data from sensorunit 111 and employs neural networks, artificial intelligence, fuzzysystems, or probabilistic systems to aid in medical decision making fortherapy recommendations or diagnosis.

Optionally additional information or test data helpful for diagnosis ortreatment may be gathered from electronic files or user input from anoutside source via and stored in data storage 105. Additionalinformation or test data may include: patient age, height, weight,symptoms, allergies, diet, previous or present medications, medical orfamily history of disease, sickness or infection, results of previousblood, urine or other bodily fluid analysis, or other nongenetic (e.g.,environmental) or immunological factors relating to the patient.

Optionally systems-biology platform 104 sends therapy recommendations ordiagnosis report to an outside source via wireless communication 106 andstore recommendations or report in data storage 105.

In clinical, managed-care, hospital, diagnostic, therapeutic, orbiomedical application or embodiment, systems-biology platform 104,using one or more firmware, source or object code software, configurablelogic chip or device, digital signal processor, systolic processingarray, or other finite state machine, actually or effectively maycompare set of bioinformatic values associated with sensor signal orsimulation data, preferably associated with same or different temporalstates, to determine or otherwise recognize one or more genomic mutationassociated with or corresponding to target patient, animal, plant, orother biological host.

Furthermore systems-biology platform 104 may operate autonomously, incooperation with other computer system nodes, clients, or processingelements, to collect, process and display various host or patient sensoror simulation data, preferably in combination.

For example patient information and other personal or medical recorddata may be received via questionnaire or otherwise retrieved, such ashost identification, drug treatment, prescription, and dosage, single ormultiple concomitant food or drug allergy, interaction or side effect,pregnancy, lactation, as well as bioinformatic, genetic, proteomic,metabolomic, and other monitored, simulated or sensed mutation-relateddata as described herein.

Systems-biology platform 104 may be used in time-critical emergency,urgent, or trauma situation to improve patient health-care diagnosis andtreatment, for example, by early-detection, expediting and assistingphysician, paramedical, nursing, or other professional analysis andtreatment.

Sensed signal or simulated data as electronically may be labeled forindicating genomic mutation, significantly improves quality and accuracyof medication delivery and administration to identified subgroups ofpatients having certain adverse response to medication, food, or othertreatment.

Additionally such data or signal may include pharmaco-genomic orpharmaco-kinetic clinical or indications based on genetic, proteomic,metabolomic (i.e., analysis of small organic cell molecules andmetabolic response thereof), or other bioinformatic variant or mutation,or other genetic-based condition or profile (e.g., sex, race/ethnicity,etc.) such as drugs to be avoided, or considered as alternative. Thusoptimally host susceptibility or predisposition to toxicity or otheradverse host reaction or side effects to certain identified food, drugs,or other medical treatment may be minimized, mitigated, or eliminatedusing automated rule-based advise or expert system.

For example, systems-biology platform 104 may alert medicalprofessionals when host patient is determined via sense or simulationapproach to detect genomic mutation that patient ability to producethiopurine S-methyltransferase (TPMT) enzyme activity is compromised.TPMT genetic test (commercially available from DNA Sciences (Raleigh,N.C.) enables identification of patient at risk for6-MP/azathioprine/thioguanine toxicity, and improves confidence throughtailored dosing regimens, while minimizing concern over drug-inducedcomplication.

Alternatively, genomic mutation to G protein-coupled receptors (GPCR)molecular target and variant alleles may be detected to electronicallylabel and thereby effectively modify host drug therapy. Another genomicmutation that may be detected and labeled is enzyme debrisoquinehydroxylase (CYP2D6), isozyme of microsomal cytochrome P450monooxygenase system; encoding gene is located in CYP2D gene cluster incontiguous 45-kb region of chromosome 22. Here, at least ninepolymorphisms of CYP2D6 affect metabolism of more than 30 differentpharmaceuticals, including 13-adrenergic receptor antagonists,neuroleptics, and tricyclic antidepressants.

Systems-biology platform 104 may couple electronically or digitally tohospital, physician, nursing, or other medical staff communicationsystem to enable network-accessible prescription renewal, appointmentscheduling, lab-result entry or retrieval, referrals to specialists anddisease management, as well as generally computerized physician orpharmacy-ordering scheme, patient communications, access to medicalsimulation, test or sensor results, insurance claim status, andbar-coding of pharmaceuticals, and automated medication checks forpossible errors.

System-biology platform 104 may employ simple identical or substantialequivalent value check between recently-measured value andpreviously-stored value for same host, for example, after host exposureto radiation or other carcinogenic sources. Such algorithm may beexecuted to adapt iteratively or dynamically in real-time or in multipleor parallel processors based on currently or recently-measured,monitored, or sensed host bioinformatic values, for example using fuzzysystem, Bayesian or neural network, to improve compute or processingperformance by comparing initially values that previously are known orrecorded to be related or likely to be related or otherwise weighted tosensor signal or simulation data.

Additionally electronic access to sensor signal or simulation data maybe restricted, secured, encrypted, or excluded unless the host thereofexplicitly or voluntarily provides prior informed consent to access suchinformation.

Hence, comparison serves to detect presence or absence of target sensorsignal, simulation data or other genomic or bioinformatic value (e.g.,oncogene, tumor suppressor gene, allele, enzyme, repeat sequence,micro-deletion, or other mutant gene product, protein, or metabolome)that causes, or increases or decreases risk of one or more host disease,disorder, syndrome, allergy, or other biological condition.

Such simulation data or sensor information may be stored in data storage105 or in other digital storage accessible or otherwise retrievablethrough network 103. Such stored information may be formatted accordingto one or more conventional, industry-standard, or otherwise publicly orcommercially-available software, processing, storage, and communicationsprotocol, as well as databases for metabolic, signaling, regulatory andpathway data.

Additionally other genomic relational or object-oriented knowledge baseor data sources may be network-accessed, such as GenBank, Unigene,LocusLink, Homologene, Ensemble, GoldenPath, or NCICB Cancer GenomeAnatomy Project (CGAP). Such information may be accessed usingontology-based interfaces that are defined to be logically related, forexample, using annotation format such as Distributed Annotation System(DAS).

Optionally systems-biology platform 104 data or instructions may bespecified and otherwise annotated, such as hypothesis definition,experiment design, sample preparation and distribution, experiment run,data acquisition, result analysis, data mining, design refinement,modeling, knowledge discovery, or project report. Additionally suchfunctions may be applied to simulation data or sensor signal processedby software or hardware analysis tools, e.g., for pharmacogenomics, geneexpression, high-throughput sequencing, or proteomics (functional orstructural) use-case domains.

Preferably such stored information complies, at least in part, with dataexchange and management framework and specifications provided byInteroperable Informatics Infrastructure Consortium (I3C), whichtechnical and use-case model documents, and recommended implementations,as described on-line at http://www.i3c.org/ are hereby incorporated byreference as appropriate herein.

For example, one or more I3C-compliant or recommended data format may beemployed during operation of electronic label processor, as describedherein. Accordingly simulation data or sensor signal may be accessed,and displayed or otherwise imaged using electronic display I/O hardwareor software, for gel chromatography images, original data frombiological arrays, arrays of time-series data from mass spectrometry,illustrative functional depiction of proteins, simple microscope images,patient records with medical images, derived data from multiple ortime-series images, electrocardiograms, or original drawings andannotations to medical images made by examining professionals. On-screensearch capability enables medical professional quickly to locate andinterpret particular host simulation data or sensor signal, such as genesequence, protein, enzyme, allele, or other related detail.

Network 103 access to various databases or other digital repository maycouple in n-tiered architecture multiple client interfaces, servecomponents, back-end objects and data sources. For example, Linux-based,Netscape, or Microsoft Internet Explorer browsers or applications, e.g.,based on Java, non-Java, Perl, C, C++, or other programming ordevelopment software, run on client nodes 60 may receive information,such as in various markup-language, e.g., HTML, XML, etc., from back-endobjects over conventional network messaging or transport protocol, e.g.,hyper text transfer protocol (HTTP), TCP Internet Protocol, simpleobject access protocol (SOAP), file transfer protocol (FTP), IIOP, etc.Additionally Universal Description Discovery Integration (UDDI) registryand Resource Description Framework (RDF) agent advertising formats maybe used.

Further genomic, proteomic, or metabolomic sequence analysis softwaretool, for example, (e.g., BLAST, TimeLogic) may be used by controller112 to discover or characterize host genomic, proteomic, or metabolomicsequence, acquired and qualified from one or more sources, such assensor unit 111 or data storage 105. Thus, internal and externalsequence and protein libraries may be updated and maintained, certainredundant, unqualified or external data being filtered for internalsequence processing. One or more target, putative or otherwise mutantgene or bioinformatic value is then determined and cataloged effectivelyby systems-biology platform 104.

Hypothetical function of such determined gene or value may be generatedmanually, automatically, or homologously by finding similarity to knownor other prior values. Genetic, proteomic, or metabolomic analysisprotocols and similarity analysis may be defined and selected, therebyenabling or constructing functional hypotheses to be generated,prioritized, or reviewed using sensor measurements or other hostevidence.

Proteolysis sample preparation may be performed (e.g., HPLC, gelelectrophoresis), then mass spectroscopy or tandem MS analysis andcompression, quantitization, and fragment size genome analysis forcandidate prediction, proteome or metabolome comparison, and otherquantitative analysis using modeling tools and databases.

Systems-biology platform 104 may receive data from sensor unit 111, andneural networks, artificial intelligence, fuzzy systems, orprobabilistic systems consider presence of conditions in diagnosis ofgenetic disorders: point mutations, mutations within non-codingsequences, deletions and insertions, trinucleotide repeat mutations,autosomal mutations, gain of function mutations, loss of functionmutations, mutations in mitochondrial genes, enzyme defects, defects inreceptors and transports systems, defects in receptors and transportsystems, alterations in structure, function or quantity of non-enzymeproteins, defects in receptor proteins, defects in protooncogenes ortumor-suppressor genes, aneuploidy, unbalanced autosome, sex chromosomeabnormality, fragile X syndrome, ring chromosome, chromosome inversion,isochromosome formation, translocation, or abnormal gene products.

Optionally allele-specific oligonucleotide hybridization may be employedin multifunctional array 200 in biosensor platform 102 to assist indirect gene diagnosis of mutations. Systems-biology platform 104 maydiagnose genetic disease or mutation, such as Mendelian disorders,autosomal dominant disorders, autosomal recessive disorders, X-linkeddisorders, Marfan syndrome, Ehlers-Danlos syndrome, familialhypercholesterolemia, lysosomal storage diseases, Tay-Sachs Disease,Gangliosidosis, Niemann-Pick disease, Gaucher Disease, glycogen storagediseases, Mucopolysaccharidoses, Alkaptonuria, Neurofibromatosis,trisomy 21, chromosome 22q11 deletion syndrome, Klinefelter syndrome,XYY syndrome, Turner Syndrome, Multi-X females, hermaphroditism,pseudohermaphroditism, triplet repeat mutations, chromosome-breakagesyndrome, Prader-Willi syndrome, Angelman syndrome, or gonadalmosaicism.

Alternatively, systems-biology platform 104 may diagnose infectiousdisease or infection, such as Haemophilus influenzae infection,tuberculosis, histoplasmosis, coccidioidomycosis, shigella bacillarydysentery, Campylobacter enteritis, Yersinia enteritis, Salmonellosis,typhoid fever, cholera, amebiasis, giardiasis, herpes, chlamydia,gonorrhea, syphilis, trichomoniasis, staphylococcal infection,streptococcal infection, clostridial infection, measles, mumps,mononucleosis, polio, chickenpox, shingles, whooping cough, diptheria,infections associated with Neutropenia and Helper-T cell depletion,cytomegalic inclusion disease, pseudomonas infection, legionnairesdisease, listeriosis, candidiasis, cryptococcosis, aspergillosis,mucormycosis, pneumocystis pneumonia, cryptosporidium and cyclosporainfection, toxoplasmosis, dengue fever, Rickettsial Infection, trachoma,leprosy, plague, relapsing fever, lyme disease, malaria, babesiosis,leishmaniasis, African Trypanosomiasis, Chagas disease, Trichinellosis,hookworm, cysticercosis, Hydatid disease, schistosomiasis, lymphaticfilariasis, or onchocerciasis.

In diagnosing infectious disease or infection, systems-biology platform104 receives data from sensor unit 111 or neural networks, artificialintelligence, fuzzy systems, or probabilitic systems that considerpresence of infectious agent, such as a prion, virus, bacteriophage,plasmid, transposon, chlamydiae, rickettsiae, mycoplasma, fungi,protozoa, helminths, or ectoparasite. In host system, systems-biologyplatform 104 may also consider the presence of bacterial endotoxin,bacterial exotoxins, proliferation and morphologic lesions of epithelialcells, tissue necrosis, granulomas, cysts, increased levels ofleukocytes, mononuclear cells or neutrophils, mononuclear interstitialinfiltrates, reduced levels of immune cells (e.g. cytokines,lymphocytes, macrophages, dendritic cells or natural killer cells),bacterial leukotoxins, hemagglutinin, spores, or other antigen orprotein from bacteria, virus, fungi, protozoa, or parasite.

Alternatively systems-biology platform 104 may diagnose disease ofimmunity, such as hypersensitivity disorders (immune complex mediated,complement-dependent reactions, cell mediated, or anaphylactic type,transplant rejection), autoimmune disease, systemic sclerosis,inflammatory myopathies, mixed connective tissue disease, polyarteritisnodosa or other vasculitides, X-linked agammaglobulinemia of Bruton,common variable immunodeficiency, isolaged IgA deficiency, Hyper IgMsyndrome, DiGeorge syndrome, severe combined immunodeficiency disease,immunodeficiency with thrombocytopenia and eczema, acquiredimmunodeficiency syndrome (AIDS), or amyloidosis.

In diagnosing immunity diseases, systems-biology platform 104 considersfollowing sensed, detected, or measured conditions from sensor unit 111:levels of immune cells (e.g., mast cells, cytokines, lymphocytes,macrophages, dendritic cells or natural killer cells), MHC (majorhistocompatibility complex) molecules or antigens, HLA (human leukocyteantigen) complex, antigens, or types, or levels of primary mediators(e.g., biogenic amines, chemotactic mediators, enzymes, orproteoglycans), secondary mediators (e.g., leukotrienes, prostaglandins,platelet-activating factors, or cytokines), histamines,platelet-activating factor (PAF), neutral proteases, chemotacticfactors, or antigen-presenting cells (APC).

In diagnosing autoimmunity diseases, systems-biology platform 104receives data from sensor unit 111 or neural networks, artificialintelligence, fuzzy systems, or probabilistic systems considers presenceof auto-antibodies disease and considers whether auto-antibodies aredirected against single organ or cell type or whether it is systemic.Autoimmune diseases include single organ or cell type related diseases(e.g., hashimoto thryoiditis, autoimmune hemolytic anemia, autoimmuneatrophic gastritis of pernicious anemia, autoimmune encephalomyelitis,autoimmune orchitis, goodpasture syndrome, autoimmune thromcytopenia,insulin-dependent diabetes mellitus, myasthenia gravis, Graves disease),or systemic autoimmune diseases (e.g., systemic lupus erythmatosus,rheumatoid arthritis, Sjögren syndrome, or Reiter syndrome).

Systems-biology platform 104 may identify whether disease condition maybe single organ or cell type autoimmune diseases or primary biliarycirrhosis, chronic active hepatitis, ulcerative colitis, or membranousglomerulonephritis. The platform is also identifies whether diseasecondition may be systemic autoimmune disease or inflammatory myopathies,systemic sclerosis (scleroderma) or polyarteritis nodosa.

Furthermore systems-biology platform 104 may determine presence ofpathologic autoimmunity by considering at least three requirements, suchas presence of autoimmune reaction, clinical or experimental evidencethat such reaction is not secondary to tissue damage but of primarypathogenetic significance, or absence of another well-defined cause ofdisease.

Alternatively systems-biology platform may be used in diagnosis ofneoplasia. In diagnosing neoplasia, systems-biology platform 104receives sensed, detected, or measured data from sensor unit 111 andneural networks, artificial intelligence, fuzzy systems, orprobabilistic systems considers the following factors: DNA damage,failure of DNA repair, mutations in the genome of somatic cells,activation of growth-promoting oncogenes, alterations in the genes thatregulate apoptosis, inactivation of cancer suppressor genes, expressionof altered gene products and loss of regulatory gene products,oncoproteins, growth factors, growth factor receptors, proteins involvedin signal transduction, nuclear regulatory proteins, cell cycleregulators, tumor antigens, or the levels of immune cells (e.g., mastcells, cytokines, lymphocytes, macrophages, dendritic cells or naturalkiller cells).

Systems-biology platform 104 may consider epidemiological factors indetermining diagnosis for neoplasia. Epidemiological factors may includecancer incidence, geographic or environmental factors (DNA damagingagents—e.g. chemicals, radiation or viruses), or heredity (e.g.,inherited cancer syndromes, familial cancers, autosomal recessivesyndromes of defective DNA repair). Systems-biology platform 104 mayconsider tumor markers such as hormones (e.g. human chorionicgonadotropin, calcitonin, catecholamine and metabolites, or ectopichormones), oncofetal antigens α-fetoprotein or carcinoembryonicantigen), isoenzymes (e.g., prostatic acid phosphatase, orneuron-specific enolase), immunoglobulins, prostate-specific antigens ormucins or other glycoproteins (e.g. CA-125, CA-19-9, or CA-15-3).

After systems-biology platform 104 makes diagnosis, platform mayrecommend treatments in combination or individually. Such recommendationmay include diet changes, surgery, radiation therapy, chemotherapy,medications, antiangiogenesis therapy, or other cancer treatment.Systems-biology platform 104 may instruct therapeutic unit 113 tomanufacture or dispense pharmaceuticals, biopharmaceuticals, or othertherapeutic tools for the treatment of neoplasia.

Systems-biology platform 104 may employ sensor device and simulationmethod for analyzing dynamic hormone-secretion phenomena in dynamicbiological systems, for example using sensor, artificial neural network,and dosing device; e.g., Sicel Technologies wireless or telemetricsensor platform for measuring parameters of relevance in vivo, such asradiation dose, tissue microenvironment or gene expression to increasetreatment success. Implantable sensors may be provided 2 mm diameter, 15mm length, for injection at margin of tumors using minimally invasiveprocedure.

Biosensor 100 may be applied to food technology, e.g., pasteurization ordevelopment or production of artisan foods. DNA sensor 201 may monitor,detect, or measure amount of bacteria or microflora used to ripen anddevelop flavors in foods, such as artisan cheese. Similarly peptide orprotein sensor 203, lipid or fatty acid sensor 208, or small moleculesensor 217 may monitor bacterial or microflora production of fats,proteins, esters, or other biologically-active molecules.

Biosensor 100 may be applied to food manufacturing industry, e.g.,quality control, food safety, or countering food borne illness caused bybioterrorism. Biosensor 100 may detect types of food contaminants,including bacteria or chemicals that cause human sickness, or counterbioterrorism acts threatening consumer food supply.

Biosensor 100 may be used by food manufacturer, crop cultivator, labresearcher, consumer, packer, distributor, receiver, food vendor, orfood inspector to ensure quality control and food safety. Biosensorplatform 102 may detect, measure, or determine presence or absence ofparasitic organism, virus, bacteria, fungi, protozoa, or unicellular ormulti-cellular organism present during food manufacturing process orgrowth of food crops, or prior to consumption.

Chemical sensor 216 may be used to sense, detect, or measure foreignchemicals, such as toxins, vitamins, minerals or other organic andinorganic chemicals. Systems-biology platform 104 may analyze raw datafrom biosensor platform 102 to identify potentially-hazardous organismor chemical or flag unknown organism or chemical.

When systems-biology platform 104 identifies or quantifies potentiallyhazardous organism or chemical or unknown organism or chemical, data isstored in storage 105. Systems-biology platform 104 may generate reportdocument or electronic multi-media warning or signal, which disclosesdetected organism or chemical and determine whether manufacturing, cropgrowth, or consumption is safe to continue.

Systems-biology platform 104 may send automated warning or signal, sentvia wireless communication 106, to information recipient interested indata gathered by the platform, such as remote database, researcher, lab,government agency, or health or safety maintenance organization.

Chemical sensor 216 may determine purity or verify amount of vitamin,mineral, herb, or botanical claimed by a food product, meal supplement,vitamin supplement, or other nutritional substance. Systems-biologyplatform 104 may compare amount of vitamin, mineral, herb or botanicaldetermined by chemical sensor 216 to pre-set amount or range stored instorage 105, e.g. amount or range determined by government agency orhealth or safety maintenance organization.

Systems-biology platform 104 generates report whether detected amount orrange complies with pre-set amount or range, and determines whethermanufacturing or consumption is safe to continue. Detected amount can bereported and sent via wireless communication 106 to outside source orinformation recipient interested in data gathered by chemical sensor216, such as packer, distributor, receiver, remote database, researcher,lab, government agency, or health or safety maintenance organization.During manufacturing, determined amount of vitamin, mineral, herb, orbotanical present in each lot or batch of produced product is recordedor accessible through network 103 for analysis.

Optionally if amount of vitamin, mineral, herb, or botanical fallsoutside pre-set amount or range, systems-biology platform 104 generatesautomated warning to outside source or information recipient. Biosensor100 monitors manufacturing of food product, meal supplement, vitaminsupplement, or other nutritional substance by ensuring that manufacturedsubstance complies with required amount or range of nutritionalsubstance. Chemical sensor 216 may be used to demonstrate whetherparticular vitamin, mineral, herb, botanic, or other natural or organicfood has properly absorbed in biological system of organism.

Biosensor 100 may synchronize different input stimuli, particularly withintegrated purpose of evaluating food and drug interactions positivelyor negatively within host. Systems-biology platform 104 can analyzegenetic composition of host, determined through DNA sensor 201, toassist in predicting particular drug-food interactions. To assist inpredicting drug and food interactions, host genetic composition may besupplemented with additional information or test data includingnongenetic (e.g. environmental, epidemiological) or immunologicalfactors relating to host.

Biosensor 100 may be implanted within a host and pharmacogenetics 304 orpharmacokinetics 308 in systems-biology platform 104 may be employed tomonitor or determine activity or effectiveness of medication usedindividually or in combination. Meanwhile, biosensor 100 placed remotelyor separately from implanted biosensor is used to analyze nutritionalsubstance (e.g., food product, meal supplement, vitamin, or mineral)that may be consumed by same host.

Data from remote biosensor 100 is coupled, received, or combined to datafrom implanted biosensor or analyzed collectively by systems-biologyplatform 104 to predict or model combined allergic reactions, sideeffects, or adverse reactions that result from consumption ofnutritional substance in conjunction temporally with related medication.

Systems-biology platform 104 may generate automated recommendation orreport diagnostically or therapeutically about optimum level ofnutritional substance or identify alternative substance for consumption.Data from remote and implantable biosensor data, and recommendation ordetermination processed by systems-biology platform 104 may be stored indata storage 105. An outside source or information recipient may accessdata and results in data storage 105 through wireless communication 106for analysis via network 103.

When systems-biology platform 104 identifies nutritional substance thatmay cause an adverse or positive reaction, automated warning or messagemay be transmitted wirelessly to information receipt interested in thegathered data. The ability of systems-biology platform to analyze ormodel nutritional substance and host condition in combination using hostsensor data and consumable sensor data optimizes treatment of real-timephysiological condition.

Biosensor 100 may be applied to biopharming purpose, e.g., field testsor inspections of genetically engineered plants, and use of geneticallyengineered plants or transgenic crops to produce therapeutic proteinsand industrial enzymes with safeguards for ensuring that food crops arenot co-mingled with food crops intended for pharmaceutical or industrialuse.

To prevent out-crossing or commingling of genetic material, DNA sensor201, RNA sensor 202, or peptide and proteins sensor 203 in biosensorplatform 102 may detect, sense or measure presence or absence of foreigngenetic material or protein in food crop not intended for pharmaceuticalor industrial use. Systems-biology platform 104 may analyze raw datafrom biosensor platform 102 to identify out-crossing or commingling ofgenetic material.

When systems-biology platform 104 identifies foreign genetic material,data is stored in storage 105. Systems-biology platform 104 may generatereport about detected foreign genetic material or determine whether cropgrowth is safe to continue. Systems-biology platform 104 may sendautomated warning or signal, via wireless communication 106, toinformation recipient interested in data gathered by platform, such asremote database, researcher, lab, government agency, or health or safetymaintenance organization.

Biosensor 100 may monitor growth of food crops, e.g., sensors (e.g.peptide or protein sensor 203, vector or virus vector sensor 207, pHsensor 212, metabolites sensor 219, etc.) in biosensor platform sensor201 may sense, detect or measure abnormalities in crop growth orreproduction. Biosensor 100 may monitor, detect or measure pesticides,insecticides or foreign chemicals effect on growth or reproduction.

Biosensor 100 may be applied to bio-manufacturing industry, e.g.,drug-producing plants and transgenic animals, such as cows geneticallytransformed to excrete different kinds of therapeutic proteins in breastmilk. Peptide or protein sensor 203 in biosensor platform 102 orantibody sensor 204 may detect or measure presence or absence ofgenetically engineered therapeutic protein or antibody in breast milk orother biological fluid.

Biosensor 100 may be applied in xenotransplantation, for example byscreening animal organs for transplantation into humans. Sensor unit 111senses, measures, or processes biological molecule, such as cell,tissue, or intracellular or extracellular material from animal cell,tissue or organ, or raw data is analyzed by system biology platform 104.System biology platform 104 analyzes or determines whether animal cell,tissue, or organ is compatible for use with human for transplantation orother therapeutic process.

Biosensor 100 may be applied to avian transgenics, particularly toproteins produced through poultry-based production systems. For example,biosensor platform 10 may detect whether successful transformation isoccurring via avian embryonic germ cell, retroviral-mediatedtransformation, sperm-mediated transgenesis, avian embryonic stem cell,direct egg transfection, or other transformation process.

Biosensor 100 may be applied to drug-producing plants, e.g., tobacco,corn, or other non-food plants, for biomanufacturing. Peptide or proteinsensor 203 may detect, sense or measure presence, absence, manufactureor biological activity of recombinant proteins manufactured in plants.DNA sensor 201, RNA sensor 202, vector or virus vector sensor 207,chromosome sensor 221, or cell sensor 222 may monitor or detect whethergenetic material, vector, chromosome, or cell successfully integrates orgenetically transforms plant or animal.

FIG. 3 c systems-biology platform 104, therapeutic unit 113, and sensorunit 111. Systems-biology platform 104 provides verification of data321, to assure that data is proper or feasible from biosensor platform102 within sensor unit 111. Verification of data 321 identifies sequenceor structures of target system. Data may be analyzed statistically bysystems-biology platform 104, using statistical computation, e.g.,scatter plot matrices, Venn diagrams, comparative histograms, volcanoplots, or gene ontology charts. Computed statistics are interpretedbiologically, filtering or reducing dataset to manageable size byeliminating results that show insignificant or uninteresting biologicaldata.

Verification of data 321 includes checking regulatory relationship ofgenes or interaction of proteins that provide signal transduction ormetabolism pathways, as well as physical structure of organisms,organelle, chromatin, cell-cell interactions, or other components.

To integrate sensor data, software and management systems are used.Systems-biology platform 104 may utilize management software, e.g.,Analysis Information Management System (AIMS), using tools to analyze ormanage range of complexity of data obtained from microarrays or assays,tracking computational processes. Data-mining tools, e.g.,high-dimensional data analysis tools, may process data where data havemultiple dimensions.

Data may be formatted using standardization programs, e.g., GeneExpression Markup Language (GEML), Microarray Markup Language (MAML),Microarray and Gene Expression Data (MAGE), MicroArray and GeneExpression Markup Language (MAGE-ML), solutions by Microarray GeneExpression Database group (MGED) or Minimum Information About aMicroarray Experiment (MAIME), or other programs.

After data is verified, modeling/simulation 322 uses combined simulationdata or sensor signal to model biological structures or relativeinteractions. Modeling or simulation 322 simulates biologicalinteractions to identify behavior of system, for example, sensitivity ofbehaviors against external perturbations and how quickly system returnsto normal state after stimuli.

Another example includes simulating how individual malfunctioningmis-folded protein interacts with other proteins or cellular components,with simulations on how protein responds to particular therapies; yetanother example is modeling phospho-proteomics and systems biologicalrole for oncology target discovery or validation.

Modeling or simulation 322 predicts methods of controlling state ofbiological system, e.g., pharmaceutical or gene therapy transformationof malfunctioning cells into healthy cells. For example throughstructural analysis, regulation of c-Ab1 and STI-571 specificity may beachieved.

Modeling or simulation 322 prediction is translated into instructionsfor therapeutic unit 113 to implement appropriate therapy to fixbiological system. These instructions are conveyed to therapeutic unit113, where instructed therapy may be performed.

Sensing unit 111 monitors progress, efficiency, or ancillary effects ofinduced therapy on biological system. Data from sensing unit 111 may beverified by verification of data 321, which provides cyclicalself-regulating process.

FIG. 4 a shows flow of instructions from systems-biology platform 104 tonetwork 103 to components comprising therapeutic unit 113. Components oftherapeutic unit 113 include therapeutic manufacture 108, therapeuticreservoirs 109, and reconfigurable sensor manufacture 110. Thesecomponents may be reconfigurable or software-programmable according tosystems-biology platform 104, or from external source through wirelesscommunication unit 106.

FIG. 4 b shows therapeutic manufacture 108 of: pharmaceuticals 401,biopharmaceuticals 402, tissue, reconfigurable biocatalytic chips 403,tissue scaffolds 404, M/N machines 405, or other therapeutic material ortools. These components may be reconfigurable and software-programmableaccording to systems-biology platform 104, or from external sourcethrough wireless communication unit 106.

Pharmaceuticals 401 may be known and matched with organism, orcomputationally derived or optimized from systems-biology platform 104.Pharmaceuticals 401 is defined herein as including chemical substancethat provides benefit to system.

Biopharmaceuticals can be naturally-occurring biological molecule orstructural derivative of biological molecule. For example,biopharmaceuticals can be isolated DNA molecules, recombinant DNAmolecules, DNA fragments, oligonucleotides, antisense oligonucleotides,RNA molecules or constructs, self-modifying RNA molecules, catalyticRNAs, ribozymes, modified ribozymes, synthetic peptides, peptidelinkers, proteins, fusion proteins, antibodies, modified antibodies,antigens, cell surface receptors, monoclonal antibodies specific forepitopes, polyclonal antibodies, tissue factors, modified tissuefactors, mutant tissue factors, ligands, vectors, virus strains for genetransfer, recombinant plant viral nucleic acids, bacterial strains,oil-body proteins as carries of high-value peptides in plants, hostcells, transformed cells, or microorganisms newly isolated in pure formfrom natural source.

Therapeutic unit 108 may prepare biopharmaceutical product such as 2 vgof sub50-nm tenascin nanocapsules containing antisense of protein kinaseCK2 α subunit or similarly GFP and RFP-labeled bacteria which producetoxins or other therapeutic proteins to be used to target tumors.Further therapeutic unit 108 can perform functions like so-calledIntelligent Pill (e.g., University of Calgary) in which informationrelayed to chip that controls micropumps that squeeze-out therapeuticmaterial.

Therapeutic manufacture unit 108 may prepare therapy comprisingpharmaceutical 401 or biopharmaceutical aspect 402. For exampleantiangiogenesis therapy using yttrium-90 nanoparticles with conjugatedanti-Flk-1 monoclonal antibody administered by i.v. injection isanti-angiogenic agent for treatment of solid tumors. Therapeuticmanufacture unit 108 may produce small interfering RNA (siRNA) used toinhibit P-gp encoded by MDR1 gene; production enhances accumulation ofsensitivity of multidrug-resistant cancer cells to drugs transported byP-glycoprotein.

Reconfigurable biocatalytic chips 403 are software programmable frominstructions by systems-biology platform 104, or from external sourcethrough wireless communication unit 106. Depending on instructions,reconfigurable biocatalytic chips 403 can be activated, deactivated,manufactured, or disassembled. Reconfigurable biocatalytic chips 403undergo molecular bioprocessing, fabricating or manipulating single andmultienzyme systems on biochip to induce artificially biocatalysis insystem.

Tissue scaffolds 404 may be reconfigurable, and controlled bysystems-biology platform 104 instructions (or from external sourcethrough wireless communication unit 106). Scaffold 404 may be substrateto grow cells or tissues, which may be activated or deactivatedaccording to signaled instructions. Permanent or biodegradable tissuescaffolds can be used. Further scaffold 404 may be personalized bysystems-biology platform, e.g., John Hopkins University stem cell-basedpolymer scaffolds for tissue engineering using composite hydrogel. Aftermodeling tissue development on biomaterial scaffolds based onindividualized systems-biology profile, reconfigurable scaffold 404 canbe programmed with biological signals based on individual need.

M/N tools 405 may perform therapeutic treatments, e.g., Johnson &Johnson Cordis Corporation, that make drug coated stents that keeparteries from clogging by releasing medication. Examples of M/N toolsmay be self-assembling, e.g., Angstrom Medica altered calcium andphosphate molecules that self-assemble to create nanostructred syntheticbone.

Another tool example is S. Stupp project at Northwestern University,which provides long complex molecules with hydrophobic tails andhydrophilic heads; these molecules self-assemble to form cylindricalstructures that can be applied to making artificial bone. Anotherexample of M/N tools 405 is Son Binh Nguyen use of nanoparticles forsmall molecule chemotherapy, in which engineered hydrophobic cyclicpeptides attaches to targeted molecules and subsequently chemicallyreact with molecule, breaking it into pieces.

FIG. 4 c shows components of therapeutic reservoirs 109. Release oftherapies is dictated or controlled by systems-biology platform 104instructions, or from external source through wireless communicationunit 106; timing mechanisms or rate of release may be reconfigured bysoftware, e.g., MicroCHIPS implantable bioMEMS for drug delivery, inwhich silicon reservoirs hold medications in solid, liquid, or gel form,or iMEDD “NanoPORE Membranes,” silicon wafers that have channels orpores with dimensions on nanometer scale for drug release.

Pre-filled reservoirs 410 contain medication filled-in biosensor 100before implantation in living system. Contents of pre-filled reservoirs410 may be pharmaceuticals or biopharmaceuticals in active form forrelease directly to living system. Pre-filled reservoirs 410 may holdprobes, amino acids, nucleotides, or building blocks for sensormanufacture 110 for making additional biosensors.

Precursors 411 may be biological and chemical precursors to therapeuticpharmaceuticals and biopharmaceuticals. Depending on instructions fromsystems-biology platform 104, therapeutic precursors may be released, ortherapeutic manufacture 108 may process into active pharmaceuticals orbiopharmaceuticals.

Therapeutic storage 412 may store excess medication produced bytherapeutic manufacture 108. Application of storing medication ratherthan manufacturing as needed if large doses, i.e., that cannot be madefast enough by therapeutic manufacture 108, are needed at timeintervals.

FIG. 4 d shows basic components or interactions of sensor manufacture110. Systems-biology platform 104 sends software instructions to sensormanufacture 110 to dictate manufacture, disassembly, activation, ordeactivation of software-programmable biosensors. Once reconfigurablebiosensors are programmed and produced, such components and sensor datasignals are integrated, multiplexed, or processed in combination intobiosensor platform 102 for biological sensing.

Biosensor chip 421 acts as array or probe arranger 420 attaches probesonto array. Probe arranger 420 may attach probe for assaying, accordingto instructions by systems-biology platform 104. Method of attaching byprobe arranger 420 can be printing method (e.g., placing probes on arraywith automated machinery). Probes may be attached through microspotting,in which automated microarray is produced by printing small quantitiesof pre-made biochemical substances onto solid surfaces.

Printing method may be ink-jet printing, e.g., GeSiM; non-contact methodplaces probes on array, in which probes are sprayed on surface usingpiezoelectric or other propulsion to transfer biochemical substancesfrom nozzles to solid surfaces, or directly placed. This method allowsin situ synthesis, advantageously synthesizing oligonucleotideson-the-fly directly on array surface. To change DNA that may be placedon array, systems-biology platform 104 provides probe arranger 420 listof sequences to synthesize.

Another example of probe arranger 420 is photolithography, e.g.,Affymetrix GeneChips. Photolithography allows oligonucleotides to bebuilt base-by-base (e.g., proteins build amino acid-by-amino acid) onarray surface by repeated cycles of photodeprotection and nucleotide oramino acid addition. Like ink-jet printing, this process allows buildingof M/N arrays without preexisting probes and can generate probes in situon surface of biosensor chip 421.

Customizable microarray platform, e.g., CombiMatrix, includingsemiconductor-based desktop microarray platform may fabricate customoligonucleotide biochips. Microarrays with unique content are designedand fabricated on-the-fly using software driven process to generatereagents electrochemically. DNA oligonucleotides are synthesized in situaccording to probe sequence designed. Probe arranger 420 may usecell-positioning chip, e.g., Aviva chip, to provide living whole-cellarrays.

Optionally soft lithography may use stamp to pattern surfaces of array,using patterned elastomer based on program instructions to definemicrolfuidic networks on surface.

FIG. 5 shows DNA unit 500, representating organization of sensors inbiosensor platform 102, such as RNA sensor 202, peptide or proteinsensor 203, etc. DNA unit 500 may include DNA sensor 201, DNAtherapeutic manufacture 501, DNA therapeutic reservoirs 502, or DNAreconfigurable biosensor 503 together in same physical structure, whichlay in close proximity with each other. DNA therapeutic manufacture 501is structure-specific category of therapeutic manufacture 108. DNAtherapeutic reservoirs 502 and DNA reconfigurable biosensor 503 arestructure-specific categories of therapeutic reservoirs 109 and sensormanufacture 110 respectively.

Sequential steps begin with input introduction into DNA unit 500,specifically DNA sensor 201. Raw data is transferred to systems-biologyplatform 104, a remote source. Systems-biology platform 104 processesinformation, outputting data and giving instructions to DNA therapeuticmanufacture 501, DNA therapeutic reservoirs 502, and DNA reconfigurablebiosensor 503. DNA therapeutic manufacture 501, DNA therapeuticreservoirs 502, and DNA reconfigurable biosensor 503 perform instructedtasks, with DNA sensor 201 monitoring respective progress.

DNA sensor 201 monitors or senses organism response to therapiesdispensed by DNA therapeutic manufacture 501, DNA therapeutic reservoirs502, or DNA reconfigurable biosensor 503. Proximity of DNA sensor 201,DNA therapeutic manufacture 501, DNA therapeutic reservoirs 502, or DNAreconfigurable biosensor 503 within same unit facilitates monitoringfrom DNA sensor 201.

Ongoing feedback is transmitted from DNA sensor 201 to DNA therapeuticmanufacture 501, DNA therapeutic reservoirs 502, and DNA reconfigurablebiosensor 503, while responding continually to DNA sensor 201 raw data,creating cyclic system of monitoring or responding.

FIG. 6 flow chart shows automated or computer-assisted diagnosis ortherapy recommendations or reports for target host, which is identifiedinitially for possible diagnosis or treatment 601. To determine if hostbenefits from diagnosis or treatment, host undergoes preliminaryscreening 602. Preliminary screening may be implemented through softwareform; host undergoes preliminary modeling 603.

Modeling or simulation is used to model appropriate components orcharacteristics of device. After preliminary modeling 603, behavior ofmodel is verified for accuracy 604. If behavior of model is not ok,biosensor 100 is modified 605, and preliminary modeling 603 is repeated.If behavior of model is ok 604, biosensor 100 is configured 606.Reconfigurable biosensor is made or programmed according to such model.

Reconfigurable biosensor may be verified to comply or adhere to FDAregulations 607. If biosensor does not comply or adhere, it is modified608 and configuration 606 or verification of adherence to FDAregulations 607 is repeated. If biosensor does comply or adhere to FDAregulations, it is implanted or attached to host. 609.

Biosensor is initialized to allow sensor or detection activity in vivo610. Sensing or software is executed 611. Initialization of biosensorand execution of sensing or software may operate in sequential order orin parallel. Once biosensor and software is initialized, initial in vivosensing begins 612. Sensor data is then used for in vivo modeling 613via systems-biology platform 104. After in vivo modeling 613, biosensor100 generates diagnosis or therapy recommendation 614.

Therapy recommendations may result in commands to therapeutic unit 615for therapeutic manufacturing or dispensing. Ongoing feedback betweeninitial in vivo sensing 612, diagnosis or therapy recommendations 614,or commands to therapeutic unit creates an automated sensing, modeling,and treatment cycle.

Foregoing descriptions of specific embodiments of the invention havebeen presented for purposes of illustration and description. They arenot intended to be exhaustive or to limit the invention to precise formsdisclosed. Modifications and variations are possible in light of aboveteaching.

Embodiments were chosen or described in order to explain principles andapplication of the invention, thereby enabling others skilled in the artto utilize the invention in various embodiments or modificationsaccording to particular purpose contemplated. Scope of the invention isintended to be defined by claims appended hereto and equivalents.

1. A computer-based method for predicting therapeutic strategycomprising the steps of: representing one or more biological processesand molecular interactions as mathematical models; accessing a systemsbiology platform via a network to said modeled biological processes andmolecular interactions in a biological system, wherein the systemsbiology platform comprises genomics database, transcriptomics database,proteomics database, metabolomics database, pharmacogenomics database,pharmacokinetics database, disease database or drug database; running aset of simulations based on the data in the models and administration ofa drug; and generating a simulation report for predicting outcome of theinteractions with the drug, thereby aiding in target discovery andvalidation.
 2. The method of claim 1 further comprising: simulatingmolecular interactions of biological processes in a metabolic, immune orgenetic disorder in a biological system.
 3. The method of claim 1further comprising: predicting one or more kinetic interactions betweena drug and biological molecules in a biological system.
 4. The method ofclaim 3 further comprising: analyzing the kinetic interactions betweenthe drug and biological molecules in the biological system.
 5. Themethod of claim 1 further comprising: predicting differential diagnosisof a metabolic, immune or genetic disorder in an identified sub-group ofbiological hosts.
 6. The method of claim 1 further comprising:predicting adverse reaction to a drug in a biological system.
 7. Themethod of claim 1, wherein the mathematical model and simulation appliesto analysis of drug toxicity in a biological system.
 8. The method ofclaim 1, wherein the generated simulation outcome is visually displayed.9. The method of claim 1, wherein the generated simulation outcome isstored in a data storage retrievable through the network.
 10. Acomputer-based method to assist in medical decision-making comprisingthe steps of: accessing electronically a storage via a network, whereinthe storage comprises genomics database, proteomics database,pharmacogenomics database, metabolomics database, pharmacokineticsdatabase, transcriptomics database, disease database or drug database;and implementing knowledge-based techniques in a systems biologyplatform to provide therapy, diagnosis or other automated feedback. 11.The method of claim 10 further comprising: using mathematical modelingand simulation of biological processes in a systems biology platform fordifferential diagnosis of metabolic, immune or genetic disorder in abiological system.
 12. The method of claim 10 further comprising:predicting one or more kinetic interactions between a drug andbiological molecules in a biological system.
 13. The method of claim 12further comprising: analyzing the kinetic interactions between the drugand biological molecules in the biological system.
 14. The method ofclaim 10 further comprising: predicting adverse reaction to a drug in abiological system.
 15. The method of claim 10 further comprising:analyzing drug toxicity in a biological system.
 16. A system forcomputational modeling of disease in a biological host comprising: aprocessor and a memory wherein the memory represents mathematically, viaa network, genomic, transcriptomic, metabolomic, pharmacogenomic orpharmacokinetic data; runs one or more simulations to analyze biologicalinteractions and predicts the effect of a drug on biological moleculesin the host, thereby aiding target discovery and validation.
 17. Thesystem of claim 16, wherein the biological interaction further comprisesmolecular interactions of biological processes in a metabolic, immune orgenetic disorder in a biological system.
 18. The system of claim 16,wherein the mathematical representation further comprises analysis ofdrug toxicity in a biological system.
 19. The system of claim 16 furthercomprising: prediction of kinetic interactions between a drug andbiological molecules in a biological system.
 20. The system of claim 19further comprising: analysis of the kinetic interactions between thedrug and biological molecules in the biological system.
 21. The systemof claim 16 further comprising: prediction of adverse reaction to a drugin a biological system.
 22. The system of claim 16 further comprising:analysis of drug toxicity in a biological system.
 23. The system ofclaim 16 further comprising: an electronic data storage to store andaccess simulation data.